ALK in the pathogenesis of cancer A thesis submitted for the degree of Doctor of Philosophy Nina Prokoph University of Cambridge Newnham College August 2020 This project has received funding from the European Union’s Horizon 2020 Marie Skłodowska-Curie Innovative Training Networks (ITN-ETN) under grant agreement No.: 675712 I Declaration I hereby declare that this thesis is the result of my own work and includes nothing which is the outcome of work done in collaboration except as declared in the preface and specified in the text. It is not substantially the same as any that I have submitted, or, is being concurrently submitted for a degree or diploma or other qualification at the University of Cambridge or any other University or similar institution except as declared in the preface and specified in the text. I further state that no substantial part of my dissertation has already been submitted, or, is being concurrently submitted for any such degree, diploma or other qualification at the University of Cambridge or any other University of similar institution except as declared in the preface and specified in the text. It does not exceed the prescribed word limit (60,000 words) for the Biology Degree Committee. Nina Prokoph II Abstract ALK in the pathogenesis of cancer1 Nina Prokoph Anaplastic Lymphoma Kinase (ALK) has been implicated in the pathogenesis of many types of cancer including Anaplastic Large Cell Lymphoma (ALCL) and neuroblastoma (NB). ALK is an ideal drug target as its endogenous expression is limited to neuronal cells during neonatal development, although resistance to ALK-targeted therapy has been observed. In this thesis I explore potential mechanisms of resistance to the ALK inhibitors that have been approved for ALK+ non-small cell lung cancer (NSCLC) including crizotinib, alectinib, ceritinib, brigatinib and lorlatinib. To define a global landscape of resistance mechanisms, patient-centric studies require many pre- and post-treatment tumour specimens taken from a sufficient number of patients, which is not possible for a rare cancer such as ALK+ ALCL or ALK driven NB. Hence, genome-wide CRISPR overexpression screens were conducted in ALCL and NB cell lines. We show that resistance to ALK inhibition by crizotinib in ALCL can be driven by aberrant upregulation of interleukin-10 receptor alpha (IL10RA). Elevated IL10RA expression rewires the STAT3 signalling pathway bypassing otherwise critical phosphorylation of STAT3 by NPM1-ALK. IL-10RA expression does not correlate with response to standard chemotherapy in paediatric patients suggesting that a combination of crizotinib with chemotherapy could prevent ALK-inhibitor resistance-specific relapse. In the case of ALK-driven NB resistance to ALK inhibition is associated with expression of the serine/threonine-protein kinase PIM1. While both ALK-driven and ALK-negative NB cells were insensitive to several small-molecule pan-PIM kinase inhibitors, knockdown of PIM1 by RNA interference sensitized cells to ALK inhibition and the combination of ALK inhibitors with the PIM1 inhibitor AZD1208 demonstrated mild synergy. Therefore, our data suggest the potential for combined pharmacological inhibition of ALK and PIM1 in patients with ALK-driven NB. Finally, given the above investigations largely focused on cell line-based models whereby in vitro culture conditions may cause rapid phenotypic and genotypic divergence of patient-derived cells from the originating tumour, we developed two paediatric ALK+ ALCL patient-derived xenograft (PDX) models from liquid biopsy samples of chemotherapy-refractory and crizotinib resistant patients. In vivo investigation showed that second generation ALK inhibitor brigatinib led to a reduction in the mean tumour volume relative to either vehicle or crizotinib treatment. This suggests brigatinib as a treatment option for crizotinib resistant ALCL patients. In summary, this study has identified potential mechanisms of ALK inhibitor resistance particularly in NPM1-ALK positive ALCL and ALK-driven NB. III Acknowledgements I have been extremely lucky to meet wonderful people, who have guided and supported me on my scientific journey. I will name them in the order that they crossed my way. Ralf Jauch opened the door for my research adventure when he invited me to join Prasana Kolatkar’s lab in Singapore. Eight years later he remains my untouchable role model of a supervisor. He has an indescribable talent to create the most encouraging and collaborative work environment one can dream of: Kamesh Narasimhan taught me that there is nothing wrong about feeling stupid. Calista Keow Leng Ng’s heartily guidance taught me all the wet-lab knowledge that I still cherish from. Elena Haas worked alongside me when I moved back to Germany to continue with my Master’s. Together we learned to acknowledge negative results as important results. Jianming Liu accepted me for my final Master thesis project and this way set the ground stone for two fruitful years at AstraZeneca. He was the most caring of my supervisors. Neil Henderson introduced me into the world of biomarkers and clinical samples and helped me tremendously to develop better documentation techniques. John Steele played a major part in my overall happiness and development when he supported me to move from Gothenburg to Shanghai. I am immensely grateful for his encouragement, suggestions, and perspective. I always experience an energetic happiness even days after meeting him. Although not superman, John is my hero. Xiaolin Zhang was a wonderful host and allowed me to play in his research paradise. He is bursting with energy – if I would have only met him once in my life, his image would still have stayed in my memory. But what made the most impression on me was his modesty. Qiuli Guo’s natural cheerfulness, and her mode of interaction with scientific colleagues completed my experience in the Middle Kingdom. Suzanne Turner attracted me to move to the United Kingdom and provided me with complete independence and freedom. I am thankful for everything I could learn that way. Nicola Probst has been the most important person during my PhD. She was my sunshine during an extremely work-intensive time in the lab. Isaia Barbieri infected me with fresh enthusiasm and energy. It is such a pleasure to show him new results as his creative mind finds new avenues in a matter of seconds and his feedback is the most encouraging that I have ever encountered. There are three people that inspired me without their knowledge: Katarina Höll, Donatella Luciani and Sofia Chen. IV The labs based at the Addenbrooke’s hospital have been filled with people, past and present, that made sure I kept the same level of sanity. Specifically, Jamie Matthews and Rogier ten Hoopen shared kindness and laughter with me; Hugo Larose was irreplaceable as he called out when I was getting demotivated, working too hard or being not rational or focused; Jun Mun Liew was a calm anchor; Sorcha Forde was my companion while carrying out the CRISPR screens and this way halved my suffering; Liam Lee established numerous methods that I hugely profited from. The biggest joy of my PhD has been to work alongside my undergraduate students Tu Truong, Nicola Probst, Guido Lewik, Tugce Gül and Klaas Bahnsen. They have been great teachers. In this context, I would like to thank the German Academic Exchange Service (Deutscher Akademischer Austauschdienst, DAAD), the Bayer Science and Education Foundation, Erasmus+, the German Academic Scholarship Foundation (Studienstiftung des deutschen Volkes) and Research Internships in Science and Engineering (RISE) worldwide for supporting my students financially. Both DAAD and RISE worldwide are funded by the German Federal Ministry of Education and Research. In addition, I would like to thank the European Union for their generous financial support under the Horizon 2020 Marie Skłodowska-Curie Innovative Training Network with grant agreement no. 675712. Being part of a group of 15 PhD students across Europe taught me a great deal about the value of collaboration in science. Most importantly, I would like to thank Stephen Ducray, Huan-Chang Liang, Ivonne Montes-Mojarro, Cosimo Lobello, Geeta Sharma and Serena Stadler for their support and Roberto Chiarle and Luca Mologni for been fantastic role models. The first part of my PhD I spend at Cambridge Life Sciences in Ely: Special thanks goes to Keith Rawson and Danielle Mack for their time and patience. It feels like I have spent a decade in a dark tissue culture room, which I could not have endured without music from the Red Hot Chili Peppers, Kurt Vile, Bon Iver, Hozier, Cigarettes After Sex, Mumford & Sons, Of Monsters and Men, The Lumineers, The Decemberists, and Hollow Coves. Finally, I would like to thank the patients and families that have provided samples. V Declaration of Assistance Received The following people contributed to the success of my PhD projects. I thank each of you for your time and friendliness. Experiments or data analysis carried out by students that worked under my supervision: 1) Klaas Bahnsen (University of Dresden, Germany): • Analysis of all microarray data 2) Tugce Gül (University of Bonn, Germany): • Construction and validation of NSCLC cell line H3122 stably expressing lenti dCAS-VP64_Blast and lenti MS2-P65-HSF1_Hygro • Design and introduction of guide sequences into lenti sgRNA(MS2)_Puro backbone vector for the following genes: KIT, MET, EGFR, ABCB1, KRAS, HER-2, IGF-1R 3) Guido Lewik (University of Bochum, Germany): • RT-qPCR based validation of CRISPR hits using TKI-resistant ALCL cell lines 4) Nicola Probst (University of Applied Sciences Biberach, Germany): • Establishment of alectinib-resistant and crizotinib-resistant SUDHL-1 cell line • RT-qPCR based validation of CRISPR hits using TKI-resistant ALCL cell lines • Design and introduction of guide sequences into lenti sgRNA(MS2)_Zeo backbone vector for the following genes: STAT3, NPM1, MYC, MKNK1, IL10RA, IL10, P2RY6, SH2D2A, FOXN2, HELZ2, HOXD8, RORC, GPR161, PRKACA, ADORA2A, PGBD1, HDAC8, ABCB1, EGFR, KRAS, ALK, HER-2, IGF-1R, KIT, MET • Functional validation of candidates identified from the screens in SU-DHL-1 and Mac-2A. 5) Tu Truong (University of Applied Sciences Biberach, Germany): • Construction and validation of Mac-2A and SU-DHL-1 cell lines stably expressing lenti dCAS- VP64_Blast and lenti MS2-P65-HSF1_Hygro • Optimization of Golden Gate Cloning using BsmbI for the introduction of guide sequences into lenti sgRNA(MS2)_Zeo backbone vector Experiments or data analysis carried out by collaborators: 1) Liam Lee (University of Cambridge, UK) • Design and introduction of guide sequences into lenti sgRNA(MS2)_Zeo backbone vector coding for the following genes: ARHGEF9, BCL10, BDNF, COPZ2, CRK, CLYBL, EGR4, EML2, EREG, ETV1, FAIM2, FOS, FOXP1, KRAS, MET, MFSD2A, MYC, NIN, NKX2-4, NPY, NR4A2, PIK3CD, PIM1, PLEKHG6, PRKACA, PRRX2, PSD2, PTGES, RORC, RRAS, SAGE1, SAMD4A, SEMA4A, SLC7A3, SPDEF, SSBP3, SURF2, UBIAD1, UTF1, YAP1. • Transformation and amplification of the human CRISPR activation library • Quantification of isolated amplicons by qPCR reactions using KAPA Library Quantification Kit • Cell-titre blue based validation of CRISPR dCas9 overexpression screen hits in SH-SY5Y VI 2) Martin Zimmerman (Hannover Medical School, Germany), Jamie D. Matthews (Univerity of Cambridge, UK): • Survival analysis 3) Serena Stadler (University of Giessen, Germany): • Analysis of 94 BFM samples with immunoperoxidase labelling technique 4) Jack Monahan (EMBL-EBI, Cambridge, UK): • RNA-seq analysis 5) Huan-Chang Liang (Medical University of Vienna, Austria): • Immunohistochemistry staining for IL-10RA & IL-10RB 6) Vikas Malik (Columbia University Irving Medical Center, USA): • ChIP-seq analysis 7) Luca Pandolfini (University of Cambridge, UK): • Analysis of Human Protein Atlas RNA-seq datasets 8) Shi-Lu Luan (University of Cambridge, UK): • Design and introduction of guide sequences into the lentiCRISPR v2 backbone vector coding for IL10RA 9) Stephen P. Ducray, Hugo Larose (University of Cambridge): • RT-qPCR based investigation of ALK knockdown with an inducible shRNA/crizotinib inhibition on IL10, IL10RA and IL10RB mRNA expression 10) Geeta Sharma (University of Milano-Bicocca, Italy): • RT-qPCR of lorlatinib-resistant K299 xenograft tumours 11) Ricky Trigg (University of Cambridge, UK): • Illustrations: Figure 1, Figure 4, Figure 14, Figure 19A, Figure 21, Figure 29J, Figure 31B • Cell-titre blue based PIM1 inhibitor validation in several NB cell lines. 12) Ivonne Montes-Mojarro (Tübingen University Hospital, Germany), Lakshmi Venkatraman (Royal Belfast Hospital for Sick Children, UK), Sandra Högler (Medical University of Vienna, Austria), Simone Tangermann (Medical University of Vienna, Austria), Lukas Kenner (Medical University of Vienna, Austria), Liz Hook (University of Cambridge, UK), Olivier Giger (University of Cambridge, UK): • Pathological assessment, quantification and image generation of tissue microarrays 13) Elif Karaca & Qi Wang (Boston Children’s Hospital and Harvard Medical School, USA) • Genome-wide Cas9 mini knockout screen and analysis 14) Jamie Matthews, Ricky Trigg, Harriet Kendrick-Thomas, Fallon Miller, Laura O'Reilly and Holly Bloy (University of Cambridge, UK): • Mouse handling Collaborators that generously provided patient samples, reagents, equipment and/or data: 1) Andishe Attarbaschi (St Anna Children's Hospital, Vienna, Austria) 2) AstraZeneca 3) Isaia Barbieri (University of Cambridge, UK) VII 4) Assaf C. Bester (Beth Israel Deaconess Medical Center, USA) 5) Laurence Brugières (Gustave Roussy Cancer Center, France) 6) G.A. Amos Burke (Addenbrooke’s Hospital, UK) 7) David R. Camidge (University of Colorado Cancer Center, USA) 8) Roberto Chiarle (University of Torino, Italy) 9) Ming-Qing Du (University of Cambridge, UK) 10) Falko Fend (Tübingen University Hospital, Germany) 11) F. Hoffmann-La Roche: Johannes Noe, Eveline Nüesch, Malgorzata Nowicka 12) Carlo Gambacorti-Passerini (University of Milano-Bicocca, Monza, Italy) 13) Birgit Geoerger (Gustave Roussy Cancer Center, France) 14) Inflection Biosciences 15) Andrea Janíková (University Hospital Brno, Czech Republic) 16) Robert Johnston (Royal Belfast Hospital for Sick Children, UK) 17) Wolfram Klapper (UKSH Campus Kiel, Germany) 18) Anne Lambilliotte (Hôpital Jeanne de Flandre, France) 19) Judith Landman-Parker (Hospital Armand Trousseau, France) 20) Olaf Merkel (Medical University of Vienna, Austria) 21) Luca Mologni (University of Milano-Bicocca, Monza, Italy) 22) Matthew J. Murray (Addenbrooke’s Hospital, UK) 23) Hélène Pacquement (Institut Curie, France) 24) Shahid Pervez (Aga Khan University Hospital, Pakistan) 25) Pfizer 26) Šárka Pospíšilová (CEITEC, Czech Republic) 27) Gudrun Schleiermacher (Institut Curie, France) 28) Owen Smith (Our Lady’s Children’s Hospital, Ireland) 29) Gilles Vassal (Gustave Roussy Cancer Center, France) 30) Wilhelm Woessmann (University Hospital Hamburg-Eppendorf, Germany) 31) Kent Yip (Ipswich Hospital NHS Trust, UK) Technical service providers used: 1) DNA Sequencing Facility (Department of Biochemistry, University of Cambridge, UK): • Sanger Sequencing reactions of PCR amplicons or plasmids 2) NIHR BRC Cell Phenotyping Hub (Department of Medicine, University of Cambridge, UK): • FACS cell sorting on an FACSAria™ Fusion 2 (BD Biosciences) 3) Massachusetts General Hospital Next Generation Sequencing Facility (Department of Molecular Biology, Harvard University, USA): • HiSeq RapidRun reactions 4) Human Research Tissue Bank team (Addenbrooke’s Hospital, Cambridge, UK): • Embedding of formalin-fixed tissue & sectioning of FFPE-blocks • Staining of FFPE tissue sections for ALK and CD30 VIII Table of Content DECLARATION ........................................................................................................................................ I ABSTRACT ............................................................................................................................................. II ACKNOWLEDGEMENTS ....................................................................................................................... III DECLARATION OF ASSISTANCE RECEIVED...................................................................................... V LIST OF FIGURES ............................................................................................................................... XIII LIST OF TABLES ................................................................................................................................. XV ABBREVIATIONS ............................................................................................................................... XVII CHAPTER 1 INTRODUCTION ........................................................................................................... 1 1.1 ANAPLASTIC LYMPHOMA KINASE (ALK) IN THE PATHOGENESIS OF CANCER .................................. 2 1.2 ALK+ ALCL ............................................................................................................................. 5 1.2.1 Clinical Features of Paediatric ALCL ................................................................................. 5 1.2.2 Frontline Treatment for Paediatric ALCL ........................................................................... 7 1.2.3 Treatment of Refractory/Relapsed Disease .................................................................... 14 1.3 ALK-DRIVEN NB ..................................................................................................................... 19 1.3.1 Clinical Features of NB .................................................................................................... 19 1.3.2 ALK in NB ........................................................................................................................ 19 1.3.3 Targeting ALK in ALK-driven NB ..................................................................................... 20 1.4 ALK+ NSCLC ....................................................................................................................... 22 1.4.1 Clinical Features of ALK+ NSCLC ................................................................................... 22 1.4.2 Targeting ALK in ALK+ NSCLC ....................................................................................... 22 1.5 RESISTANCE MECHANISMS TO ALK INHIBITORS ........................................................................ 23 1.5.1 ALK-dependent resistance mechanisms ......................................................................... 23 1.5.2 ALK-independent resistance mechanisms ...................................................................... 25 1.6 APPROACHES TO IDENTIFYING ACQUIRED RESISTANCE MECHANISMS TO TKIS ............................ 25 1.6.1 CRISPR-based genome-wide screens ............................................................................ 27 1.7 AIMS OF THE PHD .................................................................................................................. 30 CHAPTER 2 MATERIALS & METHODS .......................................................................................... 31 2.1 KEY REAGENTS AND RESOURCES ........................................................................................... 32 2.2 PATIENT SAMPLES .................................................................................................................. 40 2.2.1 NHL-BFM90 trial cohort ................................................................................................... 40 2.2.2 NHL-BFM95 trial cohort ................................................................................................... 41 2.2.3 ALCL99 trial cohort .......................................................................................................... 42 2.2.4 UK cohort ......................................................................................................................... 43 2.2.5 MAPPYACTS trial cohort ................................................................................................. 44 2.2.6 Brno cohort ...................................................................................................................... 45 IX 2.2.7 Pakistan cohort ................................................................................................................ 46 2.2.8 Vienna cohort ................................................................................................................... 46 2.3 ANIMAL STUDIES .................................................................................................................... 47 2.3.1 Generation of lorlatinib-resistant K299 xenografts .......................................................... 47 2.3.2 Generation of ALCL PDX ................................................................................................. 48 2.4 IMMUNOPEROXIDASE LABELLING TECHNIQUE FOR ANTI-ALK AUTOANTIBODY DETECTION ........... 49 2.4.1 Preparation of COS-1 NPM1-ALK transfectants ............................................................. 49 2.4.2 Immunostaining of COS-1 NPM1-ALK transfectants....................................................... 49 2.5 PROTEIN MICROARRAY ASSAY FOR ALK AUTOANTIBODY DETECTION ....................................... 50 2.5.1 Antigen spotting process ................................................................................................. 50 2.5.2 Processing of microarray slides ....................................................................................... 50 2.6 CELL LINES AND CELL CULTURE ............................................................................................... 51 2.7 IC50 DETERMINATION .............................................................................................................. 51 2.8 CELLULAR PROLIFERATION ...................................................................................................... 51 2.9 APOPTOSIS ANALYSIS ............................................................................................................. 51 2.10 GENERATION OF TKI-RESISTANT ALCL CELL LINES .................................................................. 51 2.11 SEQUENCING OF THE NPM1-ALK KINASE DOMAIN REGION ....................................................... 52 2.12 RT-QPCR ............................................................................................................................. 52 2.13 WESTERN BLOT ..................................................................................................................... 54 2.14 SGRNA CLONING .................................................................................................................... 55 2.15 GENOME-SCALE CAS9 TRANSCRIPTIONAL ACTIVATION SCREEN ................................................. 57 2.15.1 Genome-scale Cas9 transcriptional activation screen design .................................... 57 2.15.2 Generation of dCas9 and MS2 expressing cell lines .................................................. 59 2.15.3 Transformation, amplification and preparation of lentiviral sgRNA libraries ............... 59 2.15.4 Transduction of ALCL cell lines using lentiviral CRISPR libraries .............................. 60 2.15.5 Preparation of HiSeq libraries for the screen readout ................................................. 60 2.16 GENOME-SCALE CAS9 MINI KNOCKOUT SCREEN ....................................................................... 62 2.16.1 Genome-scale Cas9 knockout screen design ............................................................. 62 2.16.2 CRISPR Mini Knockout Screen ................................................................................... 63 2.17 SHORT-HAIRPIN RNA (SHRNA) KNOCKDOWN .......................................................................... 66 2.18 DRUG SYNERGY EXPERIMENTS ............................................................................................... 66 2.19 IMMUNOHISTOCHEMISTRY (IHC) .............................................................................................. 66 2.20 CHROMATIN IMMUNOPRECIPITATION (CHIP) QPCR .................................................................. 67 2.21 ENFORCED IL10RA OVEREXPRESSION .................................................................................... 68 2.22 MRNA SEQUENCING ............................................................................................................... 68 2.23 BIOINFORMATICS ANALYSIS ..................................................................................................... 68 2.23.1 ChIP-seq Data Analysis .............................................................................................. 68 2.23.2 Survival Analysis ......................................................................................................... 68 2.23.3 CRISPR Overexpression Screen Deconvolution and Analysis ................................... 69 2.23.4 CRISPR Mini Knockout Screen Deconvolution and Analysis ..................................... 69 X 2.23.5 mRNA-Seq Data Analysis ........................................................................................... 69 2.23.6 Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) Analysis .............. 69 2.23.7 Gene Expression Analysis .......................................................................................... 69 2.23.8 Co-Expression Analysis .............................................................................................. 70 2.23.9 Analysis of Public Gene Expression Datasets ............................................................ 70 2.23.10 Waterfall Plot ................................................................................................................... 70 CHAPTER 3 BYPASS RESISTANCE LANDSCAPE TO CRIZOTINIB INHIBITION IN ALCL......... 71 3.1 INTRODUCTION ....................................................................................................................... 72 3.1.1 Aims ................................................................................................................................. 73 3.2 VALIDATION OF THE DCAS9-VP64 INDUCED OVEREXPRESSION PHENOTYPE .............................. 73 3.3 CRISPR OVEREXPRESSION SCREENS IDENTIFY GENES MODULATING CRIZOTINIB SENSITIVITY IN ALCL CELL LINES ............................................................................................................................... 74 3.4 VALIDATION OF CANDIDATE GENES IDENTIFIED IN THE SCREEN .................................................. 76 3.4.1 Overexpression-based validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Cell Lines ............................................................................................................................ 76 3.4.2 Knockout-based validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Cell Lines ...................................................................................................................................... 79 3.4.3 Validation of Candidate Genes Modulating ALK TKI Sensitivity in Resistant ALCL Cell Lines……………………………………………………………………………………………………….81 3.4.4 Validation of Candidate Genes Modulating ALK TKI Sensitivity in a Resistant Orthotopic ALCL Cell Line Xenograft Model .................................................................................................. 81 3.4.5 Validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Patients ......... 82 3.5 DISCUSSION ........................................................................................................................... 86 CHAPTER 4 IL10RA MODULATES ALK TKI SENSITIVITY IN ALK+ ALCL ................................... 87 4.1 INTRODUCTION ....................................................................................................................... 88 4.1.1 Aims ................................................................................................................................. 89 4.2 IL10RA IS EXPRESSED IN ALCL IN AN NPM1-ALK-INDEPENDENT MANNER .............................. 89 4.3 IL10RA OVEREXPRESSION MODULATES SENSITIVITY TO ALK INHIBITION ................................. 92 4.4 KNOCKOUT OF IL10RA/IL10RB/IL10 FURTHER SENSITIZES ALCL CELLS TO ALK INHIBITION ..... 97 4.5 STAT3 IS ACTIVATED INDEPENDENTLY OF NPM1-ALK THROUGH THE IL10/IL10R SIGNALING PATHWAY ON CRIZOTINIB INHIBITION .................................................................................................... 97 4.6 HIGH EXPRESSION OF IL10RA AT DIAGNOSIS IS NOT PREDICTIVE OF CLINICAL OUTCOME FOR PATIENTS TREATED WITH STANDARD CHEMOTHERAPY ........................................................................ 101 4.7 DISCUSSION ......................................................................................................................... 103 CHAPTER 5 BRIGATINIB IS EFFECTIVE IN A PDX OF CRIZOTINIB-RESISTANT ALK+ ALCL 104 5.1 INTRODUCTION ..................................................................................................................... 105 5.1.1 Aims ............................................................................................................................... 106 5.2 PATIENT TREATMENT HISTORY AND SAMPLE COLLECTION ........................................................ 107 5.3 BRIGATINIB IS EFFECTIVE IN A PDX OF CRIZOTINIB-RESISTANT ALK+ ALCL ............................ 107 XI 5.4 DISCUSSION ......................................................................................................................... 109 CHAPTER 6 OVEREXPRESSION OF PIM1 IN ALK+ MALIGNANCIES DECREASES SENSITIVITY TO BRIGATINIB AND CERITINIB................................................................................ 113 6.1 INTRODUCTION ..................................................................................................................... 114 6.1.1 Aims ............................................................................................................................... 115 6.2 VALIDATION OF CANDIDATE RESISTANCE GENES IN ALK-DRIVEN NB CELLS EXPOSED TO ALK INHIBITORS IDENTIFIED IN A GENOME-WIDE CRISPR-CAS9 OVEREXPRESSION SCREEN ......................... 115 6.3 PIM1 INHIBITION ENHANCES THE SENSITIVITY OF HIGH-RISK ABERRANT ALK-EXPRESSING NB TO ALK INHIBITION REGARDLESS OF MYCN STATUS ................................................................................ 117 6.3.1 High expression of PIM1 in NB is associated with advanced, high risk disease independent of MYCN amplification ........................................................................................... 117 6.3.2 Inhibition of PIM1 alone lacks potency in ALK-expressing NB but enhances the efficacy of ALK inhibitors .......................................................................................................................... 117 6.3.3 Knockdown of PIM1 sensitizes NB cells to ALK inhibitors ............................................ 119 6.4 OVEREXPRESSION OF PIM1 IN ALK+ ALCL CELL LINES DECREASES SENSITIVITY TO ALK INHIBITORS ........................................................................................................................................ 119 6.5 DISCUSSION ......................................................................................................................... 120 CHAPTER 7 DETECTION AND CLINICAL SIGNIFICANCE OF ANTI-ALK AUTOANTIBODIES . 122 7.1 INTRODUCTION ..................................................................................................................... 123 7.1.1 Humoral Immune Response against ALK in ALK+ ALCL .............................................. 123 7.1.2 Humoral Immune Response against ALK in ALK+ NSCLC .......................................... 124 7.1.3 Aims ............................................................................................................................... 124 7.2 A PIPELINE TO QUANTIFY ALK AUTOANTIBODY TITRES IN ALK+ MALIGNANCIES ........................ 125 7.2.1 2D-Epoxy is the best slide activation chemistry for antigen binding.............................. 126 7.2.2 A reduced teflon mask increases the signal intensity .................................................... 127 7.2.3 Evaluation of ALK and control proteins ......................................................................... 127 7.2.4 Final slide layout ............................................................................................................ 129 7.3 PROTEIN MICROARRAY ASSAY CROSS-VALIDATION .................................................................. 130 7.4 DISCUSSION ......................................................................................................................... 134 CHAPTER 8 DISCUSSION ............................................................................................................ 135 8.1 INTRODUCTION ..................................................................................................................... 136 8.2 THE USE OF ALK INHIBITORS FOR THE TREATMENT OF PAEDIATRIC ALK+ ALCL ...................... 136 8.2.1 Crizotinib in combination with multi-agent chemotherapy could be used as a consolidation therapy before allogenic SCT for paediatric ALK+ ALCL patients after relapse .. 136 8.2.2 Brigatinib could offer a bridge to transplant for paediatric ALK+ ALCL patients after CNS relapse…………………………………………………………………………………………………...137 8.3 THE USE OF ALK INHIBITORS FOR THE TREATMENT OF ALK-DRIVEN NB .................................. 137 8.4 A COLLABORATIVE APPROACH TO COLLATE AND INTEGRATE DATA WILL BE CRUCIAL TO MAKING PROGRESS IN THE TREATMENT OF PAEDIATRIC CANCERS ..................................................................... 138 XII CHAPTER 9 APPENDIX ................................................................................................................ 139 9.1 APPENDIX 1: LIST OF PEER-REVIEWED PAPERS AND REVIEWS ................................................. 140 9.1.1 Primary research articles ............................................................................................... 140 9.1.2 Review article................................................................................................................. 140 REFERENCE LIST .............................................................................................................................. 141 XIII List of Figures Figure 1 Domain structure and aberrant forms of Anaplastic Lymphoma Kinase (ALK) .................... 2 Figure 2 Different categories of ALK+ malignancies ........................................................................... 3 Figure 3 Management of childhood ALCL ......................................................................................... 17 Figure 4 ALK in NB ............................................................................................................................ 20 Figure 5 ALK-dependent or ALK-independent resistance mechanisms in ALK+ NSCLC ............... 23 Figure 6 Types of in vitro experimental designs to identify putative TKI resistance mechanisms .... 27 Figure 7 Technologies to perturb gene function in mammalian cells for pooled genetic screens .... 28 Figure 8 The genome-scale Cas9 transcriptional activation screen employed to identify bypass resistance mechanisms to ALK TKIs ..................................................................................................... 58 Figure 9 The genome-scale Cas9 knockout screen employed to identify bypass resistance mechanisms to ALK TKIs ...................................................................................................................... 62 Figure 10 ABCB1 overexpression induces resistance to crizotinib ................................................. 74 Figure 11 The dCas9-VP64-based CRISPR activation system induces overexpression of various genes in different ALCL cell lines .......................................................................................................... 74 Figure 12 CRISPR Overexpression Screens Identify Genes Modulating Crizotinib Sensitivity in ALCL Cell Lines ..................................................................................................................................... 75 Figure 13 CRISPR Overexpression Screens Identified STAT3 and NPM1 to Modulate Crizotinib Sensitivity in ALCL Cell Lines ................................................................................................................ 76 Figure 14 Overexpression of candidate genes identified from the SAM screen induce resistance to crizotinib …………………………………………………………………………………………………………78 Figure 15 Overexpression-based validation of candidate genes ..................................................... 78 Figure 16 CRISPR knockout screen dataset by Ng et al. identifies MYC and RORC as vulnerabilities in ALK+ and ALK- ALCL ........................................................................................................................ 79 Figure 17 Knockout-based analysis ................................................................................................. 80 Figure 18 Hit validation in resistant ALCL cell lines ......................................................................... 81 Figure 19 Hit validation in a resistant orthotopic ALCL cell line xenograft model ............................ 82 Figure 20 Validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Patients ......... 84 Figure 21 Schematic of the IL10 signalling pathway ....................................................................... 88 Figure 22 IL10RA is Expressed in ALCL Patient Tumour Tissue .................................................... 90 Figure 23 IL10 signaling is represented in ALCL cell lines .............................................................. 91 Figure 24 Transcription of IL10RA is independent of NPM1-ALK expression and activity .............. 92 Figure 25 IL10RA Overexpression Modulates Sensitivity to ALK Inhibition .................................... 94 Figure 26 IL10 Overexpression does not Modulate Sensitivity to ALK Inhibition ............................ 96 Figure 27 Plasmid-based IL10RA Overexpression Modulates Sensitivity to Crizotinib Inhibition ... 96 Figure 28 CRISPR-based knockout of IL10RA/IL10RB/IL10 is not lethal, but sensitizes ALCL cell lines to ALK inhibition ............................................................................................................................ 98 XIV Figure 29 STAT3 is Activated Independently of NPM1-ALK through the IL10/IL10R Signaling Pathway on Crizotinib Inhibition .......................................................................................................... 101 Figure 30 Initial High Expression of IL10RA is not Predictive of Clinical Outcome for Patients Treated with Chemotherapy .............................................................................................................................. 102 Figure 31 Established cell lines maintain crizotinib responsiveness of the original tumour .......... 108 Figure 32 Tumour volume over time in MGS-A-x PDX mice ......................................................... 110 Figure 33 Brigatinib is effective in the treatment of a PDX of crizotinib-resistant ALK+ ALCL ...... 111 Figure 34 Validation of CRISPR dCas9 overexpression screen hits in SH-SY5Y and CHLA-20 cells …………………………………………………………………………………………………………………..116 Figure 35 High expression of PIM1 in NB is associated with advanced, high risk disease independent of MYCN amplification ......................................................................................................................... 117 Figure 36 Response of ALK+ and ALK- NB cell lines to PIM inhibition ......................................... 118 Figure 37 ALK inhibitors and AZD1208 exhibit mild synergism in KELLY cell lines ...................... 118 Figure 38 Knockdown of PIM1 sensitizes NB cells to ALK inhibitors ............................................ 119 Figure 39 Overexpression of PIM1 in ALK+ ALCL cell lines decreases sensitivity to ALK inhibitors …………………………………………………………………………………………………………………..120 Figure 40 Production, quality control (QC) and processing of microarray slides ........................... 125 Figure 41 Effect of slide activation chemistry ................................................................................ 126 Figure 42 Effect of the Teflon mask ............................................................................................... 127 Figure 43 Antigens utilized in the microarray assay ...................................................................... 128 Figure 44 A typical slide layout ...................................................................................................... 129 Figure 45 Correlation between anti-ALK autoantibody titre and anti-ALK autoantibody concentration levels in paediatric ALK-positive ALCL patients enrolled in the ALCL-99 trial .................................... 131 Figure 46 Outcomes of paediatric ALK+ ALCL patients according to the magnitude of the antibody response to ALK .................................................................................................................................. 132 Figure 47 Outcomes of paediatric ALK+ ALCL patients according to the magnitude of the antibody response against ALK in combination with their minimal disseminated disease (MDD) status .......... 133 XV List of Tables Table 1 ALK+ NSCLC inhibitor landscape ......................................................................................... 4 Table 2 Definition of clinical terms in NHL .......................................................................................... 6 Table 3 Treatment outcomes for paediatric patients with ALCL after frontline multi-agent chemotherapy with or without methotrexate (MTX) or vinblastine (VBL) ................................................ 7 Table 4 Treatment strategies for childhood ALCL ............................................................................. 8 Table 5 Past, ongoing and planned clinical trials for paediatric ALCL ............................................. 10 Table 6 Factors used for stratification of paediatric ALK+ ALCL patients into different risk groups 11 Table 7 Clinical trials evaluating ALK inhibitors in NB ..................................................................... 21 Table 8 Sensitizing (S) and resistance (R) mutations to ALK inhibitors .......................................... 24 Table 9 Previously published CRISPRn screens on drug resistance using GeCKO A v2 and B libraries…………………………………………………………………………………………………………. 29 Table 10 Previously published CRISPRa screens on drug resistance ............................................... 30 Table 11 Key Reagents and Resources ............................................................................................. 32 Table 12 Clinical trials from which samples have been acquired ....................................................... 40 Table 13 Clinical Information of Paediatric ALCL Patients Recruited onto the NHL-BFM90 Trial...... 41 Table 14 Clinical Information of Paediatric ALCL Patients Recruited onto the NHL-BFM95 Trial...... 42 Table 15 Clinical Information of Paediatric ALCL Patients Recruited onto the ALCL99 Trial that provided FFPE tissue specimens .......................................................................................................... 43 Table 16 Clinical Information of the Paediatric ALCL Patient from the UK Cohort ............................. 44 Table 17 Clinical Information of Paediatric ALCL Patients Recruited onto the MAPPYACTS Trial ... 44 Table 18 Clinical Information of T-cell Lymphoma Patients from the Brno Cohort ............................. 45 Table 19 Clinical Information of T-cell Lymphoma Patients from the Pakistan Cohort ....................... 46 Table 20 Clinical Information of T-cell Lymphoma Patients from the Vienna Cohort ......................... 47 Table 21 Starting and final TKI concentrations used to generate TKI resistant ALCL cell lines......... 52 Table 22 PCR Using Q5 High-Fidelity DNA Polymerase .................................................................... 52 Table 23 PCR cycle conditions used to amplify the NPM1-ALK kinase domain region ..................... 52 Table 24 RT-qPCR primers ................................................................................................................. 53 Table 25 List of antibodies used to detect proteins by Western blot .................................................. 54 Table 26 Oligonucleotides used to generate dsDNA fragments containing the 20 bp target sequence……………………………………………………………………………………………………….. 55 Table 27 Phosphorylation and annealing of single-stranded sgRNA oligonucleotides ...................... 57 Table 28 Golden Gate assembly ......................................................................................................... 57 Table 29 PCR cycle conditions used for Golden Gate assembly ....................................................... 57 Table 30 Antibiotic concentrations that were used for the selection of the transduced cell lines ....... 59 Table 31 PCR amplification of virally integrated guides ...................................................................... 60 Table 32 Oligonucleotides used for HiSeq library preparation ........................................................... 61 XVI Table 33 PCR cycle conditions used to amplify the sgRNAs’ guide sequence region and to append the Illumina (HiSeq) compatible adapters and barcodes ............................................................................ 61 Table 34 sgRNAs cloned for the CRISPR Mini Knockout Screen ...................................................... 63 Table 35 Oligonucleotides used for HiSeq library preparation ........................................................... 65 Table 36 Oligonucleotides used to generate dsDNA fragments containing the target shRNA sequence that were cloned into pLKO.1-puro ........................................................................................................ 66 Table 37 Antibodies used to detect proteins by IHC ........................................................................... 67 Table 38 ChIP-qPCR Sequencing Primers ......................................................................................... 68 Table 39 Candidate genes and their relevance in ALCL and other cancers ...................................... 77 Table 40 Baseline characteristics of Paediatric ALCL Patients Recruited to the ALCL99 Trial ....... 130 XVII Abbreviations AITL Angioimmunoblastic T-cell lymphoma ALCL Anaplastic large cell lymphoma ALK Anaplastic lymphoma kinase ANOVA Analysis of variance ATC Anaplastic thyroid cancer b.i.d. bis in die (lat), twice a day BSA Bovine serum albumin BV Brentuximab vedotin CAS9 CRISPR-associated protein 9 CNS Central Nervous System COG Children’s Oncology Group CR Complete response CRISPR Clustered regularly interspaced short palindromic repeats CRISPRa CRISPR activation CRISPRi CRISPR interference CRISPRn CRISPR nuclease DLBCL Diffuse large B cell lymphoma dCAS9 Dead Cas9 ECACC European collection of authenticated cell cultures ED50 Median effective dose EDTA Ethylenediaminetetraacetic acid EFS Event-free survival EICNHL European Inter-group for Childhood Non-Hodgkin Lymphoma EML4 Echninoderm microtubule-associated protein-like 4 ESCC Oesophageal squamous cell carcinoma FBS Fetal bovine serum gDNA Genomic DNA GeCKO Genome-Scale CRISPR Knock-Out GOF Gain-of-function GSEA Gene set enrichment analysis HL Hodgkin Lymphoma HR Hazard ratio HSF1 Heat shock transcription factor 1 ICE Ifosfamide, carboplatin and etoposide IHC Immunohistochemistry IMT Inflammatory myofibroblastic tumour InDels Insertions or deletions XVIII IRC Independent review committee ITCC Innovative Therapies for Children with Cancer JAK-STAT Janus kinase-signal transducer and activator of transcription JUN Jun proto-oncogene KO Knockout KRAB Krüppel-associated box LB Lysogeny broth LOF Loss-of-function MAGeCK Model-based analysis of genome-wide CRISPR/Cas9 knockout MAPPYACTS Molecular Profiling for Pediatric and Young Adult Cancer Treatment Stratification MDD Minimal disseminated disease MLE Maximum likelihood estimation MOI Multiplicity of Infection MRD Minimal residual disease MTX Methotrexate NB Neuroblastoma NCBI National Center for Biotechnology Information NHEJ Non-homologous end joining NHL Non-Hodgkin Lymphoma NHL-BFM Non-Hodgkin Lymphoma-Berlin-Frankfurt-Münster NMD Non-sense-mediated decay NPM Nucleophosmin NSCLC Non-small cell lung cancer NT Non-targeting ORF Open reading frame ORR Overall response rate OS Overall survival PAM Photospacer adjacent motif PBS Phosphate-buffered saline PCA Principle component analysis PD Progressive Disease PD-L1 Programmed death-ligand 1 PDX Patient-derived xenograft PFS Progression-Free Survival PI Propidium iodide PR Partial Response PTC Premature termination codon PTCL-NOS Peripheral T-cell lymphoma not otherwise specified QC Quality control RCC Renal cell carcinoma XIX RIPA Radioimmunoprecipitation assay RMC Renal medulla carcinoma RNAi RNA interference RTK Receptor tyrosine kinase SAM Synergistic activation mediator SCT Stem Cell Transplantation SD Standard deviation SFOP French Society for Paediatric oncology sgRNA Single guide RNA shRNA short-hairpin RNA SOC Serous ovarian carcinoma TAE Tris-acetate-EDTA TKI Tyrosine kinase inhibitor tracrRNA trans-activating CRISPR RNA TSS Transcription start site VBL Vinblastine WES Whole exome sequencing WGS Whole genome sequencing WT Wild type 1 CHAPTER 1 Introduction 2 1.1 Anaplastic lymphoma kinase (ALK) in the pathogenesis of cancer The ALK gene encodes a receptor tyrosine kinase (RTK), which consists of an intracellular tyrosine kinase domain, a trans-membrane domain and an extracellular ligand-binding domain2 (Figure 1A-B). As a gene first discovered from the investigation of the t(2;5) chromosomal translocation, generating nucleophosmin (NPM)-ALK, in anaplastic large cell lymphoma (ALCL)3 (Figure 1B), ALK has been thoroughly investigated for its oncogenic capacity2. ALK regulates cellular proliferation, apoptosis and differentiation by activation of multiple pathways4, including rat sarcoma (RAS)/mitogen-activated protein kinase (MAPK), phosphoinositide 3-kinase (PI3K)/AKT/molecular target of rapamycin (mTOR), Janus kinase (JAK)-signal transducer and activator of transcription (STAT), phospholipase Cγ (PLCγ), sonic hedgehog (SHH) and jun proto-oncogene (JUN)1-5 (Figure 1C). Figure 1 Domain structure and aberrant forms of Anaplastic Lymphoma Kinase (ALK) (A). The N-terminal extracellular domain comprises two MAM domains flanked by a low-density lipoprotein class A (LDLa) domain, and a glycine-rich (GR) domain. The C-terminal intracellular region comprises the tyrosine kinase (TK) domain. (B) In the wild-type receptor, ligand-induced dimerisation of the extracellular region permits auto- and transphosphorylation of the kinase domain and subsequent recruitment of signal transducers. Aberrant forms of ALK expressed in cancer are ligand-independent due to point mutations in the kinase domain, gene amplification, or gene fusion. (C) NPM1-ALK signals through the PLCγ/PKC, MEK/ERK, PI3K/AKT and JAK/STAT pathways. Modified from Trigg et al.6. MAM1 LDLa MAM2 GR TK TM ex tr ac el lu la r in tr ac el lu la r 0 (NH2) 1620 (COOH) P P P P wild-type point mutation amplification translocation/ inversion P P P P e.g. NPM P P P P MEK PI3K AKT mTOR PCLγ IP3 JAK STAT5B ERK BA STAT3 PKC Oncogenic pathways C 3 Thus, ALK has been implicated in the pathogenesis of many types of cancers that can be categorized by the specific types of alteration (Figure 2): translocations, overexpression and point mutations of ALK2 (Figure 1B). Figure 2 Different categories of ALK+ malignancies ALK-related malignancies that are studied in this thesis are highlighted in white. DLBCL: diffuse large B cell lymphoma; ATC: anaplastic thyroid cancer; ESCC: oesophageal squamous cell carcinoma; IMT: inflammatory myofibroblastic tumour; RCC: renal cell carcinoma; RMC: renal medulla carcinoma; SOC: serous ovarian carcinoma. Overexpression of ALK and amplification of the ALK gene have been reported in various types of cancer cell lines and patient samples, including inflammatory myofibroblastic tumour (IMT)7, neuroblastoma (NB)8, melanoma9, non-small cell lung cancer (NSCLC)10, rhabdomyosarcoma, glioblastoma2, breast cancer11, oesophageal cancer12, retinoblastoma, Ewing’s sarcoma and astrocytoma2. Point mutations and focal deletion of ALK, without translocation, have been identified in relatively limited types of cancer to date including anaplastic thyroid cancer13, NSCLC14, and in both familial15 and sporadic NB16 (Figure 4). In contrast, ALK is frequently translocated in human cancers with twenty-two fusion partner genes17 including Nucleophosmin 1 (NPM1)3, ring finger protein 213 (RNF213), 5-aminoimidazole-4- carboxamide ribonucleotide formyltransferase/IMP cyclohydrolase (ATIC), TRK-fused gene (TFG), moesin (MSN), tropomyosin 3/4 (TPM3/4), myosin heavy chain 9 (MYH9) and clathrin heavy chain (CLTC)2 in ALCL, and echninoderm microtubule-associated protein-like 4 (EML4)18, Kif5b kinesin family member 5B (KIF5B), TRK-fused gene (TFG), Kinesin light chain 1 (KLC1), protein tyrosine phosphatase non-receptor type 3 (PTPN3) and striatin (STRN) in NSCLC2. To add to the complexity, within the different ALK fusions there are examples of several breakpoint variants, as illustrated by the EML4–ALK translocations observed in NSCLC, by which multiple EML4 exon breakpoints fuse in‑frame with exon 20 of ALK19. Comparisons of the different ALK fusion proteins suggest that they display differences in ALK+ ALK mutation ALK overexpression ALK fusion- protein 4 signalling and in transforming tumourigenic potential17. The fusion partner of ALK generally determines the initiation of transcription, subcellular localization, dimerization, activation2 and therefore the molecular and physiological function of ALK. Several pharmaceutical companies have developed potent ALK inhibitors (Table 1). Table 1 ALK+ NSCLC inhibitor landscape Name Chemical structure Company Global Status Crizotinib Pfizer FDA approval for advanced NSCLC whose tumours are ALK+ (26/08/2011) or ROS1+ (11/03/2016) Ceritinib Novartis FDA approval (29/04/2014) for advanced ALK-rearranged NSCLC patients who experience disease progression on or who are intolerant to crizotinib. FDA broadens ceritinib approval to firstline treatment for ALK+ metastatic NSCLC (30/05/2017). Alectinib Hoffmann- La Roche FDA approval (11/12/2015) EU approval (21/02/2017) for advanced NSCLC whose tumours are ALK+. Lorlatinib Pfizer FDA approval (02/11/2018) for patients with ALK+ metastatic NSCLC whose disease has progressed on crizotinib and at least one other ALK inhibitor for metastatic disease or whose disease has progressed on alectinib or ceritinib as the first ALK inhibitor therapy for metastatic disease Brigatinib Takeda FDA approval (28/04/2017) for ALK+ metastatic NSCLC patients who have progressed on or are intolerant to crizotinib and lorlatinib. 5 The following paragraphs largely from sections of a review published in Cancers (Prokoph & Larose et al.)20, which can be found in Appendix 1. 1.2 ALK+ ALCL 1.2.1 Clinical Features of Paediatric ALCL In 1982, Stein and colleagues19 described tumours formed of neoplastic cells of unknown origin found in Hodgkin’s Lymphoma (HL), expressing the CD30 antigen (Ki-1, Ber-H2)21-22. Approximately 77% of these tumours also expressed a T cell antigen, 20% showed B cell antigens and the rest were of a null- cell phenotype expressing neither B nor T cell-distinguishing cell surface proteins. In 1988, the entity was for the first time described as ALCL, which is the name used to this day23. In 1989, a French group identified a translocation (t(2;5)(p23;q35)) in a subset of ALCL24-25, breakpoints of which were successfully cloned by Steve Morris and Tom Look in 1994, revealing the fusion of the nucleolar phosphoprotein gene NPM1 with that of a newly described gene, ALK3. However, it was not until 2008 that ALCL was split into two provisional entities; ALK+ ALCL and ALK- ALCL, which were confirmed in the 2017 version of the WHO classification of tumours of haemopoietic and lymphoid tissues. ALCL is primarily a paediatric tumour, accounting for 15% of all paediatric Non-Hodgkin Lymphoma (NHL) with an annual incidence ranging from 1.2 per million in children under 15 years to approximately 2 per million in young adults between 25-34 years26, with approximately 80 new paediatric cases diagnosed in Europe each year27. ALCL shows a bimodal age distribution; whilst the majority of paediatric cases are ALK+, about 50-60% of adult ALCL cases are ALK-. It is estimated that 90% of paediatric ALCL show aberrant expression of ALK fusion proteins and of those, approximately 75% express NPM-ALK28. ALK+ ALCL cases show improved survival rates over ALK- ones, although this could be due to the skewed age distribution with ALK- disease largely diagnosed in an adult population29. However, considering only paediatric cases, overall survival (OS) rates (see Table 2 for clinical terms) are still higher for ALK+ paediatric patients than for ALK- ones, with an event-free survival (EFS) of 65- 75% for ALK+ ALCL depending on the treatment regimen compared to 15-46% for ALK- ALCL30–33. 6 Table 2 Definition of clinical terms in NHL The first received response criteria for NHL were published in 199934, updated in 200735 by an International Working Group and 201336 termed as the Lugano Classification. The definition of response criteria in this table is based on the Lugano Classification36 and the refinement of the Lugano classification in the era of immunotherapy37. (*) 5-point scale according to Lugano Classification36: 1, no uptake above background; 2, uptake ≤ mediastinum; 3, uptake > mediastinum but ≤ liver; 4, uptake > liver; 5, uptake markedly > liver and/or new lesions. Clinical term Definition Overall survival (OS) The length of time from either the date of diagnosis or the start of cancer treatment that a patient is still alive38. Event-free survival (EFS) The length of time after primary cancer treatment ends that the patient remains free of complications/events that the treatment was intended to prevent/delay38. Progression-free survival (PFS) The length of time during and after cancer treatment that a patient lives with the disease but it does not get worse38. Complete Response (CR), Complete Remission (CR) A complete metabolic response measured by positron emission tomography- computed tomography (PET-CT) or a complete radiological response measured by CT36. Lymph nodes: on PET-CT, score 1, 2, or 3 with/without a residual mass on 5- point scale*; on CT, target nodes/nodal masses must regress to ≤ 1.5 cm in longest diameter37. No bone marrow involvement or extralymphatic sites involved36. Partial Response (PR), Partial Remision (PR) A partical metabolic response measured by positron PET-CT or a complete radiological response measured by CT36. On PET-CT score 4 or 5 with reduced uptake compared with baseline and residual mass(es) of any size. On CT ≥ 50% decrease in SPD (sum of the product of the perpendicular diameters for multiple lesions) of up to 6 target measurable nodes and extranodal sites36-37. Progressive Disease (PD) A progressive metabolic disease measured by positron PET-CT or a progressive disease measured by CT36. On PET-CT, score 4 or 5 with an increase in intensity of uptake from baseline and/or new fluorodeoxyglucose-avid foci consistent with lymphoma at preliminary or end-of-treatment assessment37. On CT, an individual node/lesion must be abnormal with: longest diameter > 1.5 cm and increase by ≥ 50% from product of the perpendicular diameters lowest point and an increase in longest diameter or short diameter from the lowest point (0.5 cm for lesions ≤2 cm / 1.0 cm for lesions >2 cm)37. New/clear progression of preexisiting nonmeasured lesions or regrowth of resolved lesions37. A new node > 1.5 cm or a new extranodal site > 1.0 cm (both in any axis) or < 1.0 cm in any axis or assessable disease of any size that must be unequivocal attributable to lymphoma37. New/recurrent bone marrow involvement37. Stable Disease (SD) No metabolic disease measured by positron PET-CT or a stable disease measured by CT36. Target nodes, extranodal lesions: on PET-CT, score 4 or 5 with no significant change in fluorodeoxyglucose uptake from baseline at interim or end of treatment; on CT: < 50% decrease from baseline in SPD (sum of the product of the perpendicular diameters for multiple lesions) of up to 6 measurable nodes and extranodal sites36. Allogenic SCT Stem cell transplantation that uses stem cells from a donor whose human leukocyte antigens (HLA) are acceptable matches to the patient’s. Autologous SCT Stem cell transplantation that uses a person’s own stem cells. Intrathecal injection A route of administration for drugs via an injection into the spinal canal, or into the subarachnoid space so that it reaches the cerebrospinal fluid (CSF). This way the drug is not stopped by the blood brain barrier38. Minimal disseminated disease (MDD) Detection of NPM-ALK transcript via e.g. RT-qPCR in bone marrow or peripheral blood samples from an ALK+ ALCL patient at diagnosis39. Recommended phase II dose (RP2D) Identified in phase I clinical trials, the RP2D is defined as the highest dose with acceptable toxicity40. Reduced Intensity Conditioning (RIC) RIC conditioning as tested by Fukano et al.41 in ALK+ ALCL is composed of (i) total body irradiation of ≤ 500 cGy as a single fraction or ≤ 800 cGy fractionated, (ii) < 9 mg/kg of busulfan, (iii) ≤ 140 mg/m2 melphalan,(vi) < 10 mg/kg thiotepa. BEAM conditioning Carmustine [bis-chloroethylnitrosourea=BCNU]-etoposide-cytarabine [Ara-C]- melphalan 7 1.2.2 Frontline Treatment for Paediatric ALCL Fortunately, paediatric ALCL patients are relatively chemo-sensitive with high response rates to diverse chemotherapy regimens as proven by various studies; EFS and OS vary between 65-75% and 70-90% respectively independent of treatment duration, drugs used or their dosages (Table 3)31–33,42–44. Given that ALCL was not recognised as a distinct form of NHL until 1989, most patients prior to this time would have been treated as B or T-cell NHL. The NHL-Berlin-Frankfurt-Münster (NHL-BFM) working group enrolled paediatric patients with B or T-cell NHL in three different trials (NHL-BFM83, NHL- BFM86, NHL-BFM90)33,45-46. Though the trials were not primarily aimed at ALCL, a retrospective paper found that the protocols used led to an 83% 9-year EFS, and an 9-year OS of 81% for CD30-positive ALCL patients46. Table 3 Treatment outcomes for paediatric patients with ALCL after frontline multi-agent chemotherapy with or without methotrexate (MTX) or vinblastine (VBL) Intermediate dose MTX high-dose cytarabine (IDM-HiDAC), not applicable (N/A). Reproduced from Prokoph & Larose et al.20. Study Designation Paediatric patients Treatment duration (months) EFS (-year) OS (-year) Grade 3/4 toxicity Multi- agent chemo- therapy +/- MTX NHL-BFM83, 8646 62 2-5 81% (9) 83% (9) N/A HM8931 82 8 66% (3) 83% (3) N/A UKCCSG-B- NHL-9001, 9002/9602, 900344 72 N/A 59% (5) 65% (5) One toxic death POG9315 (APO arm)42 85 11 71% (5) 88% (4) neutropenia/thrombocytopenia (35%) POG9315 (IDM- HiDAC arm)42 90 11 71% (4) 88% (4) neutropenia/thrombocytopenia (70%) CCG-594143 86 11 68% (5) 80% (5) neutropenia (82%), thrombocytopenia (66%), anemia (38%) LNH-9247 55 11 69% (5) 74% (5) neutropenia, hepatic events NHL-BFM90 (K1 arm)33 9 2-3 100% (5) N/A N/A NHL-BFM90 (K2 arm)33 65 2-3 73% (5) N/A N/A NHL-BFM90 (K3 arm)33 14 4-5 76% (5) N/A N/A EICNHL-ALCL99 (MTX1-arm)28 175 4-5 74% (2) 90% (2) hematologic toxicity (79%), infection (50%), stomatitis (21%) EICNHL-ALCL99 (MTX3-arm)28 177 4-5 75% (2) 95% (2) hematologic toxicity (64%), infection (32%), stomatitis (6%) Multi- agent chemo- therapy + VBL HM9131 82 7 66% (3) 83% (3) N/A EICNHL- ALCL99-VBL31 110 17-18 70% (2) 94% (2) neutropenia (29%) ANHL0131 (APO arm)48 64 12 74% (3) 84% (3) neutropenia (39%), infections (22%) ANHL0131 (APV arm)48 61 12 79% (3) 86% (3) neutropenia (84%), infections (43%) 8 NHL-BFM90 was the first trial to sort 89 paediatric ALCL patients into independent arms of the study (Table 3), although presence of the ALK translocation was not used as inclusion criteria for this trial33. The treatment protocol (Table 4) was based on the previous NHL-BFM studies, using retrospective results of ALCL patients enrolled in these studies. ALCL patients were enrolled into one of three arms according to disease severity: arm K1 for stages I and II if completely resected (9 patients), K2 for stage II non-resected and stage III (65 patients), and K3 for stage IV (14 patients). Because CD30-positive ALCL resembled B-cell NHL closely, the first protocol trialled was that used for B-cell NHL, which used methotrexate. Thus, the arms K1 to K3 tested increasing doses of methotrexate. NHL-BFM90 led to a 5-year EFS of 100%, 73% and 79% respectively for arms K1, K2 and K3. The treatment regimen lasted between 2 to 5 months compared to 7 or 8 months respectively for HM89 and HM91 (Table 4), which are both protocols that were tested by the French Society for Paediatric oncology (SFOP) at that time. As a result, and because the drug doses were comparatively lower – all with comparable EFS rates – the NHL-BFM working group recommended its NHL-BFM90 protocol as a gold standard31,33,49-50. Table 4 Treatment strategies for childhood ALCL Treatment strategies for childhood ALCL. ARA-C, cytarabine; BV, Brentuximab vedotin; Cyc, cyclophosphamide; CZ, crizotinib; Daun, daunorobicin; Doxo, doxorubicin; Eto, etoposide; IDM-HiDAC, intermediate dose MTX high- dose Cytarabine; Ifo, ifosfamide; I/T, intrathecal; IV, Intravenous; MTX, methotrexate; TT, topotecan; VBL, vinblastine; VCR, vincristine; VND, Vindesine. Not detailed: prednisone, prednisolone, dexamethasone and food supplements. (*) Randomized into MTX1 or MTX3 arm. Reproduced from Prokoph & Larose et al.20. Trial Acronym O th e r C y c If o D o x o E to M T X ( I/ T ) M T X ( IV ) A R A -C ( IV ) A R A -C ( I/ T ) V C R V N D V B L HM8931 HM9131 NHL-BFM90 (K1/2 arm)33 NHL-BFM90 (K3 arm)33 POG9315 (APO arm)42 POG9315 (IDM-HiDAC arm)42 CCG-594143 LNH-9247 +Daun NHL-BFM95 (R1/2)51 NHL-BFM95 (R3/4)51 EICNHL-ALCL99 (MTX1-arm)28 EICNHL-ALCL99 (MTX3-arm)28 EICNHL-ALCL99-VBL52 * ANHL0131 (APO arm)48 ANHL0131 (APV arm) COG-ADVL1212 (Course A/C/D) +CZ +TT COG-ADVL1212 (Course B) +CZ COG-ANHL12P1 (Course A) +CZ/BV COG-ANHL12P1 (Course B) +CZ/BV 9 Given the high risk of short-term side effects associated with methotrexate such as oral and gastrointestinal mucositis, sometimes leading to sepsis and toxic death51, lower concentrations of methotrexate administered in shorter pulses were applied in a subsequent NHL-BFM trial in 1995 (NHL- BFM 95, Table 4). NHL-BFM95 stratified patients into lower risk (stages I and II, arms R1 and R2) and high-risk patients (stages III and IV, arms R3 and R4). Patients in arms R1/R2 and R3/R4 were treated with 1 g/m² and 5 g/m² methotrexate infusions respectively. In both cases, half the patients were randomized to be given the infusion over 4 hours, while the other half were given the infusion over 24 hours. The trial found that the 4-hour infusion and the 1 g/m² dose were not inferior but were less toxic than the 24-hour infusion and 5 g/m² injection. The European Inter-group for Childhood Non-Hodgkin Lymphoma (EICNHL) launched the first randomized trial for ALCL patients under 22 years of age (Table 5), regardless of ALK status in 1999 – the ALCL99 trial (NCT00006455)28,53-54. ALCL99 enrolled 352 children over 7 years in 11 European countries and Japan. The trial tested four different protocols aiming to achieve three main goals: to lower the amount of methotrexate required, to rid the protocol of intrathecal injections and to test whether vinblastine could be a valuable addition to the protocol. Patients were randomly enrolled into arms methotrexate (MTX)1 and MTX3, which tested the NHL-BFM90 backbone with a 24-hour low-dose (1 g/m²) methotrexate infusion or a high-dose (3 g/m²) 3-hour methotrexate infusion (both without intrathecal injections) respectively. The trial achieved a 2-year EFS of 74.1% and a 2-year OS of 92.5%, and found that the MTX3 arm using a higher dose, but a shorter infusion time for methotrexate was overall less toxic than the MTX1 arm28,30–33,55. Thus, the investigators recommended using short-pulse, high-dose methotrexate without intrathecal injections for reduced toxicity and improved quality of life. In addition, given the comparatively lower dose of anthracycline and alkylating agent employed, it was hoped that the long-term side-effects such as obesity and metabolic syndrome would be reduced28,55-56. Besides the observed short-term toxicity, relapse compared to previous trials (HM89, HM91, NHL- BFM83, NHL-BFM86, and NHL-BFM90) averaging at 20-40% with some children experiencing multiple events28. Whilst these children tend to remain chemo-sensitive, they still suffer the long-term side effects of toxic chemotherapy55. 10 Table 5 Past, ongoing and planned clinical trials for paediatric ALCL Allo, allogeneic; AC, alectinib; auto, autologous; BV, Brentuximab vedotin; CR, ceritinib; CZ, crizotinib; Cyc, cyclophosphamide; ARA¬C, cytarabine; Dexa, Dexamethasone; Doxo, doxorubicin; Eto, etoposide; Ifo, ifosfamide; MTX, methotrexate; SCT, stem cell transplantation; TT, topotecan; VBL, vinblastine; VCR, vincristine. (*) as stated on ClinicalTrials.gov webpage. Reproduced from Prokoph & Larose et al.20. ClinicalTrials. gov Identifier Trial Acronym Treatment Phase Time frame* Location No (ALCL)* F ro n tl in e NCT00006455 EICNHL- ALCL9952,55 ALCL99 (Cyc, MTX, Ifo, Eto, ARA-C, Doxo) +/- VBL III 1999- 2005 Europe, Japan 487 NCT00059839 COG-ANHL013157 APO (Doxo, MTX, VCR) +/- VBL III 2003- 2014 USA 125 NCT01979536 COG-ANHL12P158 CZ/BV + (Dexa, Ifo, MTX, ARA-C, Eto)/(Dexa, MTX, Cyc, Doxo) II 2013- 2020 USA 140 NCT02729961 NCI-2016-0039659 BV+CR I/II 2017- 2023 USA 30 N/A EICNHL-ALCL-VBL ALCL99/VBL N/A Planne d Europe 106 R e la p s e NCT00317408 EICNHL-ALCL- RELAPSE60 allo SCT/BEAM- conditioning + auto SCT/VBL N/A 2004- 2014 Europe 96 NCT00354107 COG-ANHL06P161 SGN-30, Ifo, Carboplatin, Eto I/II 2007- 2010 USA 5 NCT01492088 C2500262 BV I/II 2012- 2018 Worldwide 36 NCT00939770 COG-ADVL091263,64 CZ I 2009- 2020 USA 26 NCT01606878 COG-ADVL121265 CZ + (Cyc, TT)/ I 2013- 2018 USA 65 NCT02034981 AcSé66 CZ Il 2013- 2022 France 24 N/A UMIN00001699167,68 (VCR,Dexa, Doxo) II 2015- 2020 Japan 10 N/A UMIN00002807569 AC I/II 2017- 2022 Japan 23 N/A ITCC053/CRISP70 CZ IB 2016- 2021* Europe 82 NCT03703050 ALCL-Nivo Nivolumab II 2018- 2026 Europe 38 NCT01742286 N/A CR I 2013- 2019 Europe 8 N/A JPLSG-ALCL-RIC18 SCT N/A 2017- 2026 Japan 18 1.2.2.1 Vinblastine: Adjusting Frontline Therapy to Reduce Relapse and Toxicity Two small retrospective studies conducted by the SFOP showed that vinblastine could reduce the risk of treatment failure, even for patients who had relapsed on chemotherapy71-72. Hence, as part of the ALCL99 protocol, vinblastine was trialled in high-risk patients (those with mediastinal, lung, liver or spleen involvement, or biopsy-proven skin lesions) who were eligible for the sub-trial, ALCL99-VBL (Table 3, Table 4). High-risk patients were first randomized into either the MTX1-VBL or MTX3-VBL arms, and then half were randomly selected to receive weekly Vinblastine at 6 mg/m², in addition to the MTX1 or MTX3 protocol they were already in, followed by weekly vinblastine only injections for 1 year on its own as a maintenance treatment52. Results showed a significant improvement over the first year of treatment with regards to EFS, but no significant difference overall with relapse being delayed rather than prevented52. Vinblastine was further trialled as a frontline therapy in the Children’s Oncology Group 11 (COG) trial ANHL0131 (NCT00059839), in addition to the chemotherapy backbone, which used low- dose methotrexate infusions – vinblastine replaced vincristine. Similar to the European trial, it did not find any significant difference between the 3-year OS or EFS as compared to standard chemotherapy, but did show that weekly vinblastine administration was more toxic than the ‘no vinblastine’ arm48. For both ANHL0131 and ALCL99-VBL, the vinblastine dose started at 6 mg/m2, but had to be reduced to 4 mg/m² due to toxicity in 41 of 61 patients. The experience with single agent vinblastine in relapse therapy (discussed below) suggested that low- dose, long-term single agent vinblastine could be as effective as is standard short-term multi-agent chemotherapy in low risk patients (see Table 6 for criteria used to stratify patients into risk groups). Therefore, a new EICNHL trial, ALCL-VBL, will investigate Vinblastine as a single-agent frontline treatment (administered weekly for 18 months, then every other week for 6 months, at 6 mg/m2), in patients negative for minimal disseminated disease (MDD), a prognostic factor previously associated with a lower risk of treatment failure73-74. The goal will be to assess whether vinblastine could replace the ALCL99 protocol, at least for low risk patients – though it may not improve the OS and EFS rates, the hope is that it will be less toxic overall. Patients who can be cured by vinblastine are spared both acute (stomatitis, neutropenia, infections, 1 – 2% treatment related mortality) and late (risk of secondary malignancies, infertility, cardiac toxicity, obesity, metabolic syndrome) toxicity of the multi-agent chemotherapy which includes etoposide, alkylators and anthracyclines. A further advantage for single agent vinblastine therapy is that patients can be treated as outpatients. Unfortunately, the long duration of the treatment protocol with weekly hospital visits for 2 years may prove to be a logistical barrier. In addition, this could provide a low toxicity chemotherapy backbone forming a new basis to study the addition of targeted therapies. Table 6 Factors used for stratification of paediatric ALK+ ALCL patients into different risk groups AIEOP, Associazione Italiana di Ematologia e Oncologia Pediatrica; BFM, Berlin-Frankfurt-Münster; MDD, minimal disseminated disease; MRD, minimal residual disease; UKCCSG, UK Children’s Cancer and Study group; SFOP, Société Française d'Oncologie Pédiatrique. Factor Relevance for risk stratification Mediastinal involvement Although in disagreement with results from the NHL-BFM-30 trial33, four independent studies by the SFOP31, the UKCCSG44, the BFM75 as well as a combined study by the three76 found mediastinal involvement to be predictive of a high risk of treatment failure. Based on those results, the ALCL99 trial used mediastinal involvement for risk stratification28,52. Visceral involvement (liver, spleen, lung) Although in disagreement with results from the NHL-BFM-30 trial33, four independent studies by the SFOP31, the UKCCSG44, the BFM75 as well as a combined study by the three76 found visceral involvement to be predictive of a high risk of treatment failure. Based on those results, the ALCL99 trial used visceral involvement for risk stratification28,52. Skin involvement Skin involvement is described as a negative prognostic marker in two BFM group studies46,75. Skin lesions were associated with increased relapse risk in a combined follow-up study of BFM, SFOP and UKCCSG76. Based on those results, the ALCL99 trial used skin involvement for risk stratification28,52. Histopathology Lymphohistiocytic subtype correlated with lower EFS in two clinical trials (HM89 and HM91)31. Small cell or lymphohistiocytic subtype was associated with increased relapse risk in the ALCL99 trial77,78 and an independent BFM group study79. Additional histology based prognostic factors were described, but not yet validated in an independent study80–86. 12 Factor Relevance for risk stratification Small cell or lymphohistiocytic subtype (SC/LC) + MDD Patients can be stratified into three biological/pathological risk groups (bpRG): high risk (bpHR): MDD+ with SC/LC subtype, low risk (bpLR): MDD- without SC/LC subtype, intermediate risk (bpIR): all remaining patients. 10-year PFS was 40%, 75% and 86% for bpHR, bpIR and bpLR, respectively78. Quantitative detection of NPM1-ALK in the bone marrow or peripheral blood at diagnosis (MDD) MDD (detected by RT-qPCR) in bone marrow samples was associated with lower 5-year PFS (41 ± 11% for MDD+ patients compared to 100% for MDD- patients)73, reduced 5-year EFS (38 ± 9% MDD+ patients compared to 82 ± 7% for MDD- patients)79 and lower 5-year OS (60 ± 9% MDD+ patients compared to 86 ± 7% for MDD- patients)79. MDD (detected by RT-qPCR) in bone marrow and peripheral blood samples was associated with lower 5-year PFS (54 ± 6% for MDD+ patients compared to 87 ± 5% for MDD- patients)39, reduced 5-year EFS (51 ± 5% MDD+ patients compared to 83 ± 5% for MDD- patients)74 and lower 5-year OS (79 ± 4% MDD+ patients compared to 91 ± 3% for MDD- patients)74. Those results were confirmed in a 10-year follow-up study of the ALCL99 trial78. Quantitative detection of NPM1-ALK in the bone marrow or peripheral blood at diagnosis during treatment course (MRD) MRD (detected by RT-qPCR) in bone marrow and peripheral blood samples before the second course of chemotherapy was associated with reduced 5-year EFS (69% MDD+ patients compared to 19% for MDD- patients)74 and lower 5-year OS (65 ± 9% MDD+ patients compared to 92 ± 5% for MDD- patients)74 and increased cumulative incidence of relapse (81 ± 8% MDD+ patients compared to 31 ± 9% for MDD- patients)74. This had first been explored in a small cohort of relapsed patients that presented with NPM1-ALK transcript level increase in bone marrow samples87. Anti-ALK autoantibody titre Anti-ALK autoantibodies can be detected in >90% of paediatric ALK+ ALCL patients at diagnosis39,88–93. Patients can be stratified into risk groups according to the anti-ALK autoantibody titre at diagnosis with cut-off at ≤ 1/750. Two studies combining patients recruited onto NHL- BFM9033/NHL-BFM9594 and NHL-BFM9594/AIEOP LNH‐9795/ALCL9928 described that titres inversely correlated with relapse risk91,92. Those results were confirmed by an independent study in Japanese children93. Course of anti- ALK autoantibody titre Patients can be stratified into risk groups according to the decrease in the anti-ALK autoantibody titre from diagnosis to the end of therapy: patients who showed a titre‐decrease of maximal two dilution steps (≤2) or patients who showed a titre‐decrease of more than two dilution steps (>2). 10-year EFS was 91 ± 5% or 70 ± 6% for patients in the ≤2 or >2 group92. Anti-ALK autoantibody titre + MDD Patients can be stratified into three biological risk groups (bRG): high risk (bHR): MDD+ and anti-ALK autoantibody titre ≤ 1/750, low risk (bLR): MDD- and anti-ALK autoantibody titre >1/750, intermediate risk (bIR): all remaining patients. 5-year PFS was 28%, 68% and 93% for bHR, bIR and bLR, respectively. 5-year OS was 71%, 83% and 98% for bHR, bIR and bLR39. Those results were confirmed by an independent study in Japanese children93. Time to relapse, failure or progression Relapsed patients can reach a second remission by chemotherapy and allogenic SCT71,96–98. Time to relapse/failure/progression serves as prognostic factor71,97,98 whereby shorter time to first relapse is an indicator for another relapse with 50% of patients that progress during first- line therapy experiencing another relapse98. Infiltration of the CNS Only a low number of patients presents with CNS infiltration95,97,99,100; in the biggest cohort analyzed so far, 26/618 (4%) patients were CNS positive100. In this study, 3-year OS after CNS relapse was 49%100. CD3 CD3+ patients that were treated with autologous SCT had lower 5-year EFS (18 ± 12% for CD3+ patients compared to 72 ± 9% for CD3- patients)97. However, no further relapse occurred in patients with CD3+ first relapse that were treated with allogenic SCT97. Based on those results, the ALCL-Relapse trial used C3 positivity for risk stratification77,98. In this study, 5/6 CD3+ patients that progressed during first-line therapy experienced another relapse compared to 3/11 CD3- patients. However, for other patient groups this effect was not pronouced98. Bone marrow involvement Depending on the analysis method used bone marrow involvement ranges from uncommon28,31–33,44,101,102 to common73,79. Due to high concordance between NPM1-ALK transcripts by RT-qPCR of bone marrow and peripheral blood samples79, Damm-Welk et al. hypothesized that this is due to circulating tumours cell rather than true bone marrow infiltration103. Bone marrow involvement (detected by RT-qPCR) was associated with a higher relapse risk (50 ± 10% for patients with bone marrow involvement compared to 15 ± 7% for patients without)79, lower 5-year PFS (41 ± 11% for patients with bone marrow involvement compared to 100% for patients without)73 and reduced OS97. 13 1.2.2.2 Development of Targeted Agents for Frontline Therapy 1.2.2.2.1 Targeting ALK With EFS and OS rates having barely changed since the NHL-BFM first tested its B-cell NHL protocol on ALCL patients in the 1980s, there is a clear need for new, less toxic therapies for patients in all risk groups. For ALCL, ALK provides an ideal drug target for those cases that are ALK+ particularly as endogenous ALK expression is limited to neuronal cells during neonatal development104 which should limit toxic side-effects. However, initial interest in the development of ALK inhibitors was largely non- existent amongst pharmaceutical companies due not only to the favourable survival rates of these patients, but also its orphan disease status. Over a decade later in 2007, ALK was identified to be fused to EML4 in 6.7% of NSCLC patients as a result of a chromosomal inversion18 and subsequently the first phase I clinical trial of Pfizer’s ALK/MET/ROS1 inhibitor crizotinib was initiated in 2008105. Other ALK inhibitors have followed and since crizotinib’s FDA approval in 2011 for advanced ALK+ NSCLC, ceritinib (Novartis) and alectinib (Hoffmann-La Roche) were likewise approved in 2014 and 2015 respectively106–108. Two more ALK inhibitors – lorlatinib (Pfizer) and brigatinib (Takeda) – have recently been granted breakthrough therapy designation and FDA accelerated approval respectively109. These drugs have been slowly filtering through to the treatment of ALCL and other ALK-related malignancies in children. Although, ALK inhibitors have been tested mainly in pediatric relapsed and refractory ALK+ ALCL patients (discussed below), the safety of crizotinib and combination chemotherapy have already been shown in a phase I trial in children with ALK-related malignancies (NCT01606878), and final data will soon be available from the phase II frontline trial of crizotinib administered in combination with ALCL99 in the USA (NCT01979536). Interestingly, a phase I/II open-label dose-finding study of ceritinib combined with Brentuximab vedotin (BV, SGN-35; discussed below) for frontline treatment of ALK+ ALCL patients 12 years and older opened recruitment in 2018 (NCT02729961). This trial will provide important information regarding new targeted agent combination strategies not involving standard chemotherapy. 1.2.2.2.2 Targeting CD30 The consistent expression of CD30 (a protein expressed almost exclusively on activated B and T cells) on ALCL provides another therapeutic target110-111. The first mouse-human chimeric anti-CD30 antibody SGN-30 was developed by Seattle Genetics and tested in a phase I/II pilot study in combination with ifosfamide, carboplatin and etoposide (ICE) in five children with recurrent ALCL (COG-ANHL06P1, NCT00354107). However, serious adverse events (pleural effusion, ascites, neutrophil count decrease, capillary leak syndrome, skin and subcutaneous tissue disorders) led to the termination of the study61. Sometime after, the activity of SGN-30 was further improved by conjugation with the antimicrotubule agent monomethyl auristatin E (MMAE). The resulting antibody-drug conjugate BV binds to CD30 on the cell surface initiating its internalization, followed by trafficking to the lysosomal compartment with eventual release of MMAE via proteolytic cleavage112. Binding of MMAE to tubulin disrupts the microtubule network, induces cell cycle arrest, and results in apoptotic death of the CD30-expressing cell113. An initial phase I clinical trial of BV (NCT00430846) was conducted in adults with CD30-positive 14 lymphomas that had failed systemic chemotherapy. The two adult patients with ALCL enrolled into the study both achieved complete remission (CR)114. Following this, a phase II study of BV in adults with relapsed or refractory systemic ALK+ and ALK- ALCL was initiated (NCT00866047)115 and in 2011 BV was approved by the FDA for the treatment of relapsed ALCL following failure of at least one multi-agent chemotherapy protocol for adults. An update to this pivotal study provided 4-year follow-up of patients included in the phase 2 study, the median Progression-Free Survival (PFS) was 20 months (25.5 months for ALK+ ALCL patients) and the 4-year OS was 64%116. Both crizotinib and BV have since been studied in adults with HL (NCT02243436, NCT01578967, NCT02098512, NCT01874054, NCT00848926 NCT02298283, NCT02227433, NCT02939014, NCT01716806) and NHL (NCT01805037, NCT02462538, NCT01657331, NCT01909934, NCT01352520, NCT01950364, NCT02139592, NCT02419287, NCT02939014, NCT00866047, NCT02280785) in a frontline setting with promising results. Additionally, BV and combination chemotherapy has been trialled in young patients with newly diagnosed HL (NCT02166463). This has encouraged a randomized phase II COG study for paediatric ALCL (COG-ANHL12P1, NCT01979536) that compares the use of BV to the ALK inhibitor crizotinib administered with a common chemotherapy backbone (ALCL99). This study is the first frontline trial of these targeted agents specifically for children with ALCL. The trial enrolled its first patient in 2013 and results will be available by the end of 2020; to date, 110 patients have been enrolled and updated study results were presented at the EICNHL meeting in November 2017. The BV arm has been closed, as recruitment is now complete; the crizotinib arm re-opened following an FDA-imposed clinical hold in March 2017 due to the occurrence of thrombosis. Catheter-related clots and pulmonary emboli occurred in 10 patients, after which the study committee initially closed the crizotinib arm, but temporarily reopened enrolment after the DSMC reviewed the cases. This is surprising as the robust and sustained activity observed in the Phase I/II COG-ADVL0912 trial (discussed below) provided the rationale for combining crizotinib at 165 mg/m2 with conventional chemotherapy. The only grade 3 or 4 drug-related adverse event was a decrease in neutrophil count occurring in 83% patients treated with 165 mg/m2 crizotinib117. In future, single agent vinblastine may provide a lower toxicity chemotherapy backbone as mentioned above. 1.2.3 Treatment of Refractory/Relapsed Disease 1.2.3.1 Stem Cell Transplantation (SCT) Some retrospective studies suggest that OS is over 50% for ALK+ ALCL relapse cases when treated with SCT or continued multi-agent chemotherapy71,118-97, the former being the standard of care for children or adolescents with some other forms of relapsed or refractory NHL (except ALCL)119-120. Therefore, one treatment option for relapsed or refractory ALCL is SCT and five retrospective studies have been conducted to assess its efficacy. The NHL-BFM working group were the first to report that SCT is a viable option for relapsed ALCL looking back at ALCL patients treated in the 1990s97,121. Two retrospective Japanese studies also found that relapsed or refractory ALCL patients who received SCT did better than those who did not118,122. For all cases, the risk profile was acceptably low, but this approach was reserved for high-risk ALCL patients who had already relapsed at least once. One of the Japanese studies in particular, showed that 30% of 15 relapsed patients treated with chemotherapy alone relapsed a second time, which is similar to the 37.5% of patients treated with autologous SCT who relapsed a second time118. However, the patient group was small with only 10 and 8 patients treated in each arm, respectively. Allogenic SCT was more successful, with all 6 patients entering remission118. Collectively, these limited data suggest that allogenic SCT is superior to autologous SCT122. A retrospective French trial also showed mixed results, with 45% of patients treated with autologous SCT entering remission, as opposed to 52% treated with chemotherapy alone71. EICNHL-ALCL-RELAPSE (NCT00317408), which between 2004 and 2011, enrolled ~105 relapsed paediatric ALCL patients sorted into three arms depending on CD3 expression and time to relapse, tested allogenic SCT and autologous SCT with and without BEAM-conditioning in comparison to single agent weekly vinblastine. For early relapsed ALCL (within the first year after initial diagnosis) autologous SCT was not effective with relapses observed in 70% of patients treated with autologous SCT in comparison to 20% of patients treated with allogeneic SCT98. Fortunately, patients that relapsed during autologous SCT could be rescued by allogeneic SCT or maintenance therapy achieving an OS rate of 80%98. Therefore, the trial established allogeneic SCT as standard consolidation therapy for ALCL patients with progression during frontline multi-agent chemotherapy or relapse after completion of multi-agent chemotherapy98. A Japanese study found 5-year EFS rates of 100% and 49%, respectively, in relapsed/refractory ALK+ ALCL patients treated with Reduced Intensity Conditioning (RIC) compared to myeloablative conditioning (MAC) regimens41. One clinical trial, JPSLG-ALCL-RIC- 18, is still ongoing and will specifically test the efficacy of RIC to prepare for allogenic SCT. 1.2.3.2 Vinblastine One arm of the EICNHL-ALCL-RELAPSE trial recruited patients with late relapse (more than 12 months from initial diagnosis) and CD3-negative ALCL to be treated with single agent weekly vinblastine for 24 months. The trial established that vinblastine achieved both high survival rates and a long-term remission rate of 81%98,123. However, vinblastine was not effective in patients that experienced a relapse within the first year after initial diagnosis98. Therefore, the authors suggest that vinblastine should only be tested in low-risk patients defined by CD3-negative relapse with more than one year after initial diagnosis98. 1.2.3.3 Development of Future Treatments for Relapsed/Refractory ALCL 1.2.3.3.1 Targeting ALK As mentioned above, COG was the first group to open a phase I dose-escalation study of an ALK inhibitor (ADVL0912, NCT00939770)63. In this trial, crizotinib was administered orally, twice daily in 28- day cycles, as a single agent for an indefinite duration to paediatric patients with ALK+ relapsed or refractory ALCL that had received at least one course of chemotherapy64. Those with relapsed ALCL achieved an objective response rate of 90%117 when treated with the recommended phase II dose (RP2D) of 280 mg/m2 63. The 10 patients treated at the RP2D in phase I of the study were included in the phase II study. The additional 10 patients that were treated at the RP2D were specifically enrolled in the phase II expansion cohort. Of the 20 patients included in the phase II expansion cohort, 13 responded within 4 weeks of initiating treatment and the remaining 7 within 5 to 8 weeks with CR in 16 18/20 patients. Two patients came off therapy after experiencing adverse events (AEs), 3 after experiencing disease progression, 12 proceeded to SCT and two continued on crizotinib117. Three years later a phase I study was also initiated by COG (COG-ADVL1212, NCT01606878) combining crizotinib with conventional chemotherapy for relapsed or refractory paediatric ALCL patients which provided the requisite safety and tolerability data for eventually integrating crizotinib into frontline treatment regimens for children with ALCL (NCT01979536). In Japan, the trials UMIN000016991 and UMIN000028075 are investigating the efficacy and safety of alectinib or crizotinib respectively as monotherapies for children with recurrent or refractory ALK+ ALCL67–69. UMIN000016991 was the first trial to test an ALK inhibitor, other than crizotinib, for paediatric ALCL patients. Based on this stuy the Ministry of Health, Labour and Welfare in Japan has approved alectinib for the treatment of recurrent or refractory ALK+ ALCL in 2020124. In addition, a first case report described the successful treatment of a girl, who suffered from a CNS relapse, with alectinib125. Results for UMIN000028075 are expected in 2022. In Europe, an Innovative Therapies for Children with Cancer (ITCC) trial is in progress to treat relapsed patients with ALK-, ROS1- or MET-positive malignancies (not limited to ALCL) with crizotinib either as a single agent or in combination with vinblastine in a phase IB safety study. The trial (ITCC053, CRISP) will determine the RP2D of vinblastine in combination with crizotinib by dose escalation of vinblastine with a fixed dose of 150 mg/m2 crizotinib. Patients will receive a maximum of 24 cycles corresponding to two years of therapy. Salvage of non-responding patients is anticipated by transfer of patients to the ALCL-Nivo trial discussed below (Figure 3). In France, the trans-tumoural, multicentric phase II trial AcSé (NCT02034981) investigates the efficacy and safety of single agent crizotinib in paediatric ALK+ ALCL patients that relapsed from chemotherapy. Although final results are eagerly awaited, preliminary results showed that 5/15 patients progressed and that all cases of progression on crizotinib occurred during the first 3 months following treatment initiation126. In addition, the EICNHL is planning to trial an ALK inhibitor in combination with the ALCL99 backbone in paediatric patients with ALK+ relapsed or refractory ALCL (personal communication with Dr. Suzanne Turner). Even though the final results from the ALK inhibitor trials are still to come, single-agent crizotinib has not yet proven curative since abrupt relapses following crizotinib discontinuation have been described in isolated cases127. Hence, crizotinib is currently used to induce second remission as shown in adult relapsed/refractory ALK+ ALCL patients before allogeneic or autologous SCT128 (Figure 3). 17 Figure 3 Management of childhood ALCL AA, auto-antibody; BV, Brentuximab vedotin; MDD, minimal disseminated disease, SCT, stem cell transplantation; TKI, tyrosine kinase inhibitor; VBL, vinblastine. Reproduced from Prokoph & Larose et al.20. 1.2.3.3.2 Targeting CD30 As mentioned earlier, following encouraging results from adult ALCL trials (NCT00430846, NCT00866047), a company-sponsored international phase I/II study of BV in paediatric patients with relapsed or refractory systemic ALCL (NCT01492088) was opened in 2012. Participants received BV 1.4 mg/kg in Phase I and 1.8 mg/kg in Phase II, on day 1 of every 21-day cycle for up to 16 cycles. Of the 17 ALCL patients recruited into the phase II expansion cohort of the trial, the Overall Response Rate (ORR) was 53% and time to progression was 6.3 months. However, 13 patients did not complete the study; 1 patient died, and 12 patients dropped out for unspecified reasons. The most common reported drug-related AE was a decrease in neutrophil and lymphocyte counts with 1 patient experiencing pyrexia and 4 of 17 patients developing neutralizing anti-therapeutic antibodies129. Additionally, a major clinical consideration is cumulative peripheral neuropathy that was observed in 36% of patients recruited onto the dose finding study of BV for adults with CD30-positive hematologic malignancies (NCT00430846)130. Given the neurologic side effect of BV, prolonged treatment may be difficult to manage in paediatric patients. Thus, this drug is currently mostly used as a bridge to transplant in relapsed ALK+ ALCL patients (Figure 3). MDD-negative ALK AA high ALK-positive ALK-negative VBL ALCL99 ALCL99 + TKI ALCL99 + BV Low-risk patient High-risk patient No response Remission Relapse MDD-positive ALK AA low BV TKI TKI + VBL ALK-negative Remission SCT Progression PDL-1 BV ALK-positive low medium high Toxicity level PDL-1 treatment strategy disease state gold standard Legend No response SCT Diagnosis: Paediatric ALCL 18 1.2.3.3.3 Immunotherapy Accumulating evidence indicates that the immune system plays a major role in the pathogenesis of ALK+ ALCL88-91. Indeed, it has been shown that ALK+ ALCL cell lines strongly express the cell surface protein, Programmed Cell-Death Ligand 1 (PD-L1; CD274, B7-H1), as determined at both the mRNA and protein levels131. Furthermore, results of PD-L1 immunostaining of ALK+ ALCL primary patient tumours showed strong PD-L1 expression132. Analysis revealed that PD-L1 expression is induced by the chimeric NPM1-ALK tyrosine kinase, via STAT3, confirming a unique function for NPM1-ALK as a promoter of immune evasion by inducing PD-L1132. PD-1 and its ligands, PDL-1 and PDL-2, have been shown to be involved in immune suppression with increased expression of PD-1 leading to decreased activation of reactive T-cells inhibiting the PI3K/AKT pathway on ligation by ligand133–135. These observations provided a strong rationale to use consolidative anti-PD1/PD-L1 immunotherapy for relapsed or refractory ALK+ ALCL. Indeed, three case reports describe a dramatic and durable response using the anti-PD1 monoclonal antibodies pembrolizumab (Merck) or nivolumab (Bristol-Myers Squibb) for ALCL patients136–138. The first, an adult with ALK- ALCL was treated with pembrolizumab following chemotherapy, BV and SCT136. The second, a 19-year old ALK+ ALCL patient was treated with nivolumab, following chemotherapy, BV, crizotinib and SCT137. Finally, a case report observed a similar dramatic response to Nivolumab in a relapsed 17-year old patient with ALK+ ALCL after two lines of treatment including chemotherapy and crizotinib138. While the 19-year old developed grade 2 pneumonitis, there are no reports of adverse events for the other two patients, pointing towards an acceptable toxicity profile. Interestingly, only the 17-year old patient was tested for PD-1 expression on tumour cells by immunostaining showing strong expression throughout the tumour. It should be noted that several publications have shown both that PD-1 inhibitors can provoke a response even in tumours which do not have strong PD-1 expression, but also that they sometimes fail in tumours which do show strong PD-1 expression139. The lack of an obvious biomarker for PD-1 inhibitor efficacy may make clinical decisions difficult when assessing therapeutic approaches for relapsed disease. With a clear need for a randomized trial of anti-PD-1 monoclonal antibodies in refractory or relapsed ALCL, ALCL-Nivo has been designed as a phase II trial of nivolumab in paediatric and adult relapsed or refractory ALK+ ALCL patients. The trial is testing the objective response to nivolumab at 24 weeks, for patients which have already relapsed on chemotherapy and either an ALK inhibitor or BV. Should there be sufficient response in this first cohort, the trial also plans to test nivolumab as a consolidation therapy after CR of at least two months as a replacement to SCT. Patients in both cohorts will be treated with 24 months of Nivolumab at 3 mg/kg every two weeks, and every four weeks after the first eight weeks for patients in the second cohort (personal communication with Laurence Brugières). Another potential immunotherapy under investigation, potentially of therapeutic use for ALCL at all stages, is the application of cancer vaccines. Strong expression of the ALK chimera in the majority of ALCL cases combined with near-absent expression of ALK in healthy tissues makes it an ideal candidate for vaccine development. Autoantibodies against ALK as well as cytotoxic and helper T cell responses to ALK have been detected in patients with ALK+ ALCL both at diagnosis and during remission with a significant inverse correlation between ALK-antibody titres and the incidence of relapse140-39. Vaccination using a truncated cDNA of ALK has been reported to induce potent and long- 19 lasting protection from local and systemic lymphoma growth in mice141-142. This has yet to be trialled in ALCL patients, but as mentioned, pre-clinical models show promising results. 1.3 ALK-driven NB 1.3.1 Clinical Features of NB NB is an embryonal tumour of the sympathetic peripheral nervous system143 accounting for 7–10% of paediatric cancers and representing 15% of all paediatric cancer deaths144–146,6. Deriving from the sympathoadrenal lineage of the neural crest, it presents along the sympathoadrenal axis147. NB is a complex disease and ranges from regression to treatment-resistant progression, metastasis and death147,6. As such, there has been a substantial collaborative effort to develop accurate risk- classification systems that will assist in determining the appropriate treatment regimen143. The COG categorizes NB patients into three prognostic groups according to the risk of death: low, intermediate, and high risk148,149. Patients are assigned to each risk group based mainly on the patient’s age at diagnosis, biological features of the tumour (e.g. tumour karyotype, MYCN amplification status, International NB Pathology Classification (INPC histopathology classification), and the stage of tumour defined by the International NB Staging System (INSS)150. Whereas low- and intermediate-risk disease is highly curable, with 5-year survival >90%, high-risk disease is associated with frequent relapse and a 5-year survival of 40–50%151,6. Considering that the majority of newly diagnosed NB patients are high- risk cases (45%) compared to 37% low risk and 18% intermediate risk cases152,153, current research efforts focus on identifying novel therapeutic approaches that will improve the survival rates and long- term effects associated with therapy for high-risk NB patients153. 1.3.2 ALK in NB To identify novel therapeutic approaches that not only achieve superior long-term survival rates but also reduce treatment-induced toxicities, many of the NB research efforts have focused on enhancing our understanding of the molecular basis of high-risk NB143. In 2013, Pugh et al. published a large (n=240) sequencing study applying a combination of whole exome sequencing (WES) (n=221), Whole genome sequencing (WGS) (n=18), or both (n=1) with all samples collected from high-risk NB patients as part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) Initiative154. The three most frequently altered genes from the TARGET study cohort were genes with previously confirmed pathogenic roles in NB16,155–158: MYCN (amplified; >20%), ALK (missense point mutations; 9%), and ATRX (multi-exon focal deletions; 7.1%). Among those genes, ALK currently represents the only directly druggable target as MYCN (transcription factor) and ATRX (chromatin remodeler) both belong to classes of proteins, which are difficult to inhibit directly159,160. Most ALK-driven NBs express full-length ALK161,162,6. In ALK-driven NB, single-base missense mutations cluster in the ALK kinase domain and promote ligand-independent signaling by disruption of the auto- inhibited conformation of the kinase163,6 (Figure 4). Mutations at three positions (R1275, F1174, and F1245) account for ~85% of ALK mutations in ALK-driven NB and are hotspots for lower frequency mutations164,6 (Figure 4B). 20 Figure 4 ALK in NB (A) Full-length ALK signals through the Ras/MAPK, PI3K/AKT and JAK/STAT pathways. In NB, MYCN expression is activated in a pathway mediated by ALK, PI3K/AKT, MEKK3, MEK5 and ERK5 (dashed lines). (B) Gain-of- function mutations cluster in the kinase domain of ALK. Modified from Trigg et al.6. NB with ALK mutations are distributed amongst all of the clinical stages155,157,165–168. However, a study of 1596 NB samples found that ALK mutations were associated with poorer survival in high- and intermediate-risk patients164,6. ALK mutations correlate with MYCN amplification and the cooperative activity of these two oncogenes as been shown to drive NB in mouse and zebrafish models169,6. Interestingly, ALK has been shown to induce transcription of MYC in NB cell lines, which could explain the poor prognosis of NB patients harbouring both MYCN amplification and ALK mutations170,6. Sequencing of matched primary and relapsed NB patient tumour samples showed an increased frequency of ALK mutations at relapse171–173,6. Some mutations were found to undergo clonal expansion at relapse, whereas others were confirmed to arise de novo174,6. These observations highlight ALK as a driver oncogene in primary and relapsed NB6. Gene amplification is a less common mechanism of ALK activation in NB (2–3% cases) leading to increased protein expression and constitutive kinase activity175,176,6 (Figure 1B). While ALK is almost always co-amplified with MYCN and therefore tumours harbouring ALK amplification tend to afford a poor prognosis158,164,177, mutation and amplification of ALK is rare in NB174,6. 1.3.3 Targeting ALK in ALK-driven NB The results of ALK inhibitor trials in ALK+ NSCLC provided the rationale for further evaluation in ALK- driven NB. In 2009, the COG initiated a phase I–II trial of crizotinib in paediatric patients with relapsed or refractory ALK+ solid tumours or ALCL (NCT00939770) (Table 7)63-64. Results from NB patients in this trial were discouraging63,6; only 1/11 had a CR and 2/11 had stable disease63. However, this is understandable, since 3/7 tumours from ALK+driven NB patients with progressive disease (PD) P P P P wild-type point mutation Ras ERK1/2 Raf PI3K AKT mTOR JAK STAT3 MEKK3 MEK5 ERK5 MYCN MEK 1116 1383 F1174C F1174I F1174L F1174S F1174V 30% F1245C F1245I F1245L F1245V 12% R1275L R1275Q 43% TK BA 21 harboured the resistance conferring ALK F1174L mutation63,6. In 2011, a phase I trial of crizotinib was launched for patients aged ≥15 years with any ALK+ malignancy other than NSCLC (NCT01121588). In addition, in 2013, COG initiated a phase I trial of crizotinib with combination chemotherapy for patients with high-risk NB and ALCL (NCT01606878)6. Encouraging results from this trial provided the rationale for a COG phase III trial evaluating the addition of crizotinib to standard therapy in high-risk NB patients with ALK mutations (NCT03126916)6. Table 7 Clinical trials evaluating ALK inhibitors in NB ALK inhibitors: Crizotinib, Ceritinib, Entrectinib, Lorlatinib; Cyclin-dependent kinase (CDK)4/6 inhibitor: Ribociclib. (*) as stated on ClinicalTrials.gov webpage. Updated from Trigg et al.6. ClinicalTrials.gov Identifier Trial Acronym Treatment Phase Time frame* Location Ref NCT00939770 COG-ADVL0912 Crizotinib I/II 2009-2018 USA 64,178 NCT01121588 PROFILE 1013 Crizotinib I 2011-2019 Worldwide 179 NCT01606878 COG-ADVL1212 Crizotinib + chemotherapy I 2013-2018 USA 180 NCT01742286 N/A Ceritinib I 2013-2019 Worldwide 181 NCT02034981 AcSé Crizotinib Il 2013-2022 France 66 NCT02650401 N/A Entrectinib I/Ib 2016-2023 USA 182 NCT02780128 NEPENTHE Ceritinib + Ribociclib I 2016-2026 USA 183 NCT03213652 N/A Ensartinib II 2017-2024 USA 184 NCT03107988 NANT2015-02 Lorlatinib +/- chemotherapy I 2017-2020 USA 185 NCT03126916 COG-ANBL1531 Crizotinib + chemotherapy III 2018-2026 USA 186 Other trials are evaluating second and third generation ALK inhibitors alone or in combination with chemotherapy and targeted agents in patients with ALK+driven NB (Table 7)6: Ceritinib187,188 is undergoing phase I assessment as a monotherapy in relapsed or refractory ALK+ paediatric cancers including NB (NCT01742286). In addition, the Next Generation Personalized Neuroblastoma Therapy (NEPENTHE) trial (NCT02780128) is recruiting patients with relapsed or refractory NB into treatment arms based on actionable genetic alterations. Patients with ALK mutations are treated with ceritinib in combination with ribociclib. The National Cancer Institute (NCI)-COG Peadiatric Molecular Analysis for Therapy Choice (MATCH) is a phase II study that involves the stratification of patients into targeted treatments based on genetic profiling (NCT03155620)189. The ALK inhibitor ensartinib is under assessment for patients with relapsed or refractory NB among other solid tumours, NHL, and histiocytic disorders with genetic alteration of ALK or ROS1 (NCT03213652). A phase I trial is assessing the TRK, ROS1 and ALK inhibitor entrectinib in paediatric patients with relapsed or refractory solid and central nervous system (CNS) tumours with and without TRK, ROS1 and ALK fusions (NCT02650401). Moreover, the New Approaches to Neuroblastoma Therapy consortium opened a phase I trial of lorlatinib in combination with chemotherapy in patients with high- risk NB (NCT03107988)6. 22 1.4 ALK+ NSCLC 1.4.1 Clinical Features of ALK+ NSCLC Based on GLOBOCAN estimates190, lung cancer is the leading cause of cancer death in both men and women worldwide191. NSCLC accounts for about 85% of lung cancer cases and remains difficult to treat, particularly in the metastatic setting192. Approximately 57% of NSCLC patients are diagnosed with metastatic, or advanced disease with a 5-year survival rate of only 5%193. Epidemiology studies suggest that approximately 3-5% of NSCLC tumours are ALK+194, which translates to approximately 75,000 newly diagnosed cases worldwide per year191. This means that NSCLC contributes by far the largest ALK fusion-positive patient population195 – the EML4–ALK fusion being the most common – with an urgent need for early disease detection. ALK+ NSCLC is associated with a younger age at diagnosis and a non-smoking history196. 1.4.2 Targeting ALK in ALK+ NSCLC Surgery remains the treatment of choice if patients are diagnosed at an early disease stage, because NSCLC patients do not respond well to chemotherapy. However, with the discovery of driver mutations, targeted therapies gave new hope to NSCLC patients with advanced and metastatic disease. After ALK was identified to be fused to EML418 in NSCLC patients, the first phase I clinical trial was initiated in 2008197. This phase I clinical trial with Pfizer’s crizotinib19,198 encouraged the initiation of a phase III, open-label trial to compare the efficacy of crizotinib to that of traditional chemotherapeutics (pemetrexed or docetaxel) in a larger NSCLC patient cohort. The ORR to crizotinib treatment was 65% (vs. 20% with chemotherapy), increasing the median PFS period to 7.7 months for the crizotinib treatment group (vs. 3 months for the chemotherapy treatment group)199,200. Crizotinib’s FDA approval in 2011 was followed by Novartis’s ceritininb in 2014 and Hoffmann-La Roche’s alectinib in 2015 (Table 1). Ceritinib was the first FDA approved drug for patients who have experienced progression on or are intolerant to crizotinib201. Ceritinib provided ORR of around 60% across the ASCEND trials in crizotinib-pretreated patients with or without brain metastases (BM)202–204. Alectinib was the first ALK-inhibitor that demonstrated superior efficacy against crizotinib in a randomized, open-label phase III trial (J-ALEX; JapicCTI-132316) in ALK+ NSCLC patients who had received a prior chemotherapy regimen. Moreover, alectinib was superior as an initial treatment compared to crizotinib in chemotherapy naïve ALK+ NSCLC patients in the global phase III ALEX trial (NCT02075840). Additionally, alectinib reduced the risk of progression by 92% compared with crizotinib in patients with brain metastases at baseline (HR =0.08; 95% CI, 0.01–0.61)205. Two more ALK inhibitors – brigatinib (Takeda, 2017) and lorlatinib (Pfizer, 2018) – have been granted breakthrough therapy designation and FDA accelerated approval. Lorlatinib – a third-generation ALK and ROS1 inhibitor – was specifically designed to increase tumour and central CNS penetration, as up to 50% of patients with ALK+ NSCLC develop CNS metastases during the course of their disease206. Brigatinib is of major interest, as it exhibited a superior inhibitory profile compared to crizotinib, ceritinib, and alectinib207 and demonstrated high activity in crizotinib/ceritinib/alectinib-refractory ALK+ NSCLC patients208. 23 1.5 Resistance mechanisms to ALK inhibitors Despite the success of ALK inhibitors, resistance inevitably develops209. For example, the median PFS after treatment with crizotinib in advanced ALK+ NSCLC patients is generally less than one year197,210– 212. Resistance to targeted therapies can be either primary/intrinsic, adaptive or acquired213. Primary resistance to a targeted therapy implies an intrinsic lack of response to the treatment. Adaptive resistance denotes disease progression after an initial response when the tumour cells undergo early adaptive changes214. Acquired resistance arises from a selection of therapy-resistant clones within a heterogeneous tumour combined with the acquisition of new alterations213. Resistance mechanisms can be classified as ALK-dependent/'on-target' or ALK-independent/'off-target' (Figure 5). On-target resistance arises, when the initial target – for example ALK – is altered, off-target resistance arises when collateral signalling events are activated214. Figure 5 ALK-dependent or ALK-independent resistance mechanisms in ALK+ NSCLC IGF1R, insulin-like growth factor 1 receptor; HER3, human epidermal growth factor receptor 3; HER2, human epidermal growth factor receptor 2; MET, hepatocyte growth factor receptor; EGFR, epidermal growth factor receptor activate downstream signals through the JAK/STAT, MEK/ERK, and PI3K/AKT and pathways. Modified from Rotow et al.214. 1.5.1 ALK-dependent resistance mechanisms ALK-dependent mechanisms can be induced by secondary mutations in ALK itself impeding the binding of the ALK inhibitor (Table 8). The prototypical mutations leading to ALK TKI resistance are gatekeeper mutations. The first one - L1196M - was reported in a crizotinib resistant EML4-ALK+ NSCLC patient215. This mutation was found to occur in 7% of ALK+ NSCLC in a first case series216. A further gatekeeper mutation - L1196Q - was initially identified in crizotinib-resistant ALCL cells in vitro217. The same paper described a I1171N mutant, which was later identified in an adult ALK+ ALCL patient progressing on crizotinib128 as well as an alectinib resistant ALK+ NSCLC patient218. Sasaki et al. described an F1174L 24 mutation in the RANBP2-ALK kinase domain of an ALK+ IMT patient219. This mutation had earlier been detected in a NB patient220 and has been shown to reduce ALK sensitivity to crizotinib by increasing ATP binding affinity221. Another mutant variant at the same position, F1174V, was also found in an ALK+ NSCLC patient resistant to crizotinib218. The secondary mutation L1152R was reported in a cell line established from a crizotinib resistant ALK+ NSCLC patient222. A number of other secondary mutations such as S1206Y, G1202R, 1151Tins or G1269A were also found in crizotinib-refractory ALK+ NSCLC patients210,223. Both, S1206Y and G1202R, are located at the solvent front of the kinase domain and interfere with inhibitor binding due to steric hindrance and conformational changes of the kinase. The G1202R mutant is only seen in ∼2% of ALK+ NSCLC patients at resistance to early-generation ALK TKIs, but it is the most common resistance mutation (21–43% of cases) in patients treated with later- generation ALK TKIs216,224. G1269A is situated at the end of the ATP-binding pocket of ALK and leads to a decrease in the binding of crizotinib to ALK due to steric hindrance223. 1151Tins, F1174C, L1152R and C1156Y near the αC helix do not directly interact with ALK TKI binding and cause resistance via conformational changes that alter kinase activity214. Another ALK-dependent resistance mechanism is amplification of ALK. Katayama et al. reported wild type EML4-ALK gene amplification in 1/15 patients that progressed on crizotinib210. Likewise, Doebele et al. documented wild type EML4-ALK gene amplification in 2/12 crizotinib resistant patients223. Genomic amplification of the ALK locus has also been described to mediate ALK TKI resistance in ALCL cell lines225,226. However, this resistance can be overcome by using increased doses of crizotinib and has not been reported as a resistance mechanism to more potent second-line ALK inhibitors227. Table 8 Sensitizing (S) and resistance (R) mutations to ALK inhibitors Mutation Biological function Crizotinib Alectinib Certinib Lorlatinib Brigatinib G1123S Steric hindrance R S R N/A N/A F1127L Decreased stability of ALK- crizotinib complex R S R N/A N/A 1151Tins Increased ATP affinity for ALK R S R N/A N/A L1152P/R Loop N-terminal of alpha C R S R S S C1156Y Loop N-terminal of alpha C, increased kinase activity R S R S S I1171T/N/S Steric hindrance R R S N/A N/A F1174V/C/L Conformational changes in the catalytic domain R S R S S V1180L Gatekeeper residue R R S N/A N/A L1196M/Q Gatekeeper residue R S S S S L1198F Near ATP-binding site, steric hindrance S R R R S G1202R Solvent front, steric hindrance R R R S S D1203N Solvent front R S S R S S1206C/Y/F Solvent front R S S S R E1210K N/A R S S S S F1245C Near the kinase motif R N/A S N/A N/A G1269A/S ATP-binding pocket R S S S S R1275Q ATP-binding pocket N/A N/A R N/A N/A 25 1.5.2 ALK-independent resistance mechanisms However, ALK-dependent mechanisms described in the previous section account for only approximately 30% of acquired resistance mechanisms noted in relapsed ALK+ NSCLC patients with the rest of the patients relapsing with various ALK-independent mechanisms (Figure 5)228–230. ALK-independent resistance is achieved by either activation of alternative RTKs or by triggering downstream signalling components231. These alterations bypass the requirement for ALK activity in the tumour cells2. Bypass mechanisms identified thus far, associated with ALK TKI resistance in NSCLC patients, include activation of EGFR227, HER2232, KRAS223, IGF-1R233, SRC210 and amplification of KIT227. Aberrantly activated ALK regulates several downstream pathways, including RAS/MAPK234, PI3K/AKT235, JAK/STAT225, PLC/PKC236 and CRKL/RAP1237 pathways. 1.6 Approaches to identifying acquired resistance mechanisms to TKIs Acquired resistance mechanisms to TKIs can be identified by three types of samples or models: 1) Patient tumour samples taken throughout a patients treatment most importantly at diagnosis and after therapy relapse, 2) mouse cancer models or 3) cancer cell lines established from patient tumour tissue238. As a gold standard, putative resistance inducers are identified most often by WES coupled with RNA- Seq, while previously identified biomarkers can be identified by targeted-seq of tumour tissue238. However, defining a global landscape of resistance mechanisms requires matched presentation-relapse tumour specimens from a sufficiently large number of patients238,230,239. For instance, the cataloguing of epidermal growth factor receptor TKI resistance in NSCLC patients with an incidence rate of 18,252– 54,756 newly diagnosed cases per year in the USA is still incomplete with around 30% of relapsed patients currently presenting with ‘unknown’ resistance mechanisms228–230. This problem is intensified for peadiatric malignancies, such as ALK+ ALCL with an incidence rate of approximately 80 newly diagnosed and 16 relapse cases per year in children and adolescents in Europe27 or ALK-driven high- risk NB with an incidence rate of approximately 30 newly diagnosed cases per year240. This has spurred the development of in vivo and in vitro experimental approaches that facilitate the proactive identification of resistance mechanisms238, validation of putative resistance mechanisms or investigation of therapeutic strategies that can re-sensitize the relapsed patients’ tumour cells to treatment241–243. The laboratory mouse (Mus musculus) is one of the best in vivo models to study disease biology including cancer. In general, three types of mouse models exist: 1) Cell line xenografts, 2) patient- derived xenografts (PDX) derived from tumour explants and 3) genetically engineered mouse models (GEMMs), which can either be transgenic or endogenous244. With the identification of the importance that tumour heterogeneity and the microenvironment play on acquired resistance to TKIs241,245–248, GEMMs have many advantages compared to cell line xenografts or cell lines238. GEMMs are also superior to PDXs in terms of microenvironment, as the human stromal components in PDXs are replaced by murine elements and PDXs lack the interaction between immune cells and tumour cells244. However, all GEMMs described to date also exhibit certain shortcomings in mimicking human malignancy arising from the fact that the biology of humans and mice is different249. For example differences in xenobiotic receptor (XR) and cytochrome P450 (CYP) expression levels, 26 tissue distribution, enzymatic activities, substrate preferences and ligand affinities lead to different rates of drug absorption, distribution, metabolism and excretion in mice and men244,250. In addition, human cells in comparison to mouse cells have decreased cancer susceptibility251. Furthermore, there is a species-specific difference in tissue-specific cancer incidence. While mice tend to develop cancer in mesenchymal tissues, most age-related cancers in humans develop in epithelial tissues249. One possible explanation is the replacement of human telomere components with murine ones since telomere dysfunction has an important role in oncogenesis and telomeres maintain chromosomal integrity244. The most artificial model, which is nevertheless predominantly used, are established cancer cell lines. Even though cancer cell lines are derived from a patient’s tumour, those cells that survive the initial culturing process represent only a small proportion of the tumour indicated by the lack of intra- heterogeneity in cell lines252–254. In addition, extended culture can induce genetic and epigenetic modifications with the eventual risk of altering cellular phenotypes255. However, cell line models require far less financial and time commitment compared to in vivo models. In addition, the patient population diversity may be better represented in vitro as there are typically far more cell lines than PDXs for each disease. For example, while only one peadiatric ALK+ ALCL PDX has been described in the literature256, 12 ALK+ ALCL cell lines had been listed by the German Collection of Microorganisms and Cell Cultures (DSMZ) by 2004257; while only two experimentally validated ALK-driven NB PDX models – COG-N- 426x (ALKF1245C; MYCN-WT) and COG-N-453x (ALKF1174L; MYCN-amplified) – have been described in the literature, >110 NB cell lines with variable ALK mutation status, had been detailed by 1998258. In vitro investigations can be further separated into three different types (Figure 6) of experimental designs 238. The first type of design involves culturing established cancer cell lines for an extended period of time during which TKI concentrations are gradually increased until resistant clones form (Figure 6A). These resistant clones are then compared to the parental TKI sensitive cells to identify potential resistance drivers via molecular and biochemical profiling procedures238. The second approach actively generates secondary mutations in the target kinase via random mutagenesis by error-prone PCR259 or by cloning the target kinase cDNA in a mutator strain of competent bacteria260 (Figure 6B). Ideally, this process will generate the full spectrum of potential secondary mutations in the target kinase gene. This mixed population of mutant kinase cDNAs should then be transfected or transduced into TKI-sensitive cancer cell lines and cDNAs that induce kinase- dependent resistance will be enriched when the cells are cultured with the respective TKI238. The last method focuses on kinase-independent (bypass) mechanisms by individually manipulating the expression level of each gene within the genome via systemic gain- or loss-of-function screen libraries (Figure 6C, Figure 7). The main advantages of this approach are the comprehensive scope of the investigation and the unbiased nature of the experiment238. It relies on pooled cDNA261, RNAi262 or Clustered Regularly Interspaced Short Palindromic Repeats (CRISPR)/CRISPR-associated protein 9 (Cas9) libraries263 (Figure 7). 27 Figure 6 Types of in vitro experimental designs to identify putative TKI resistance mechanisms (A) Cancer cells are cultured with gradually increasing concentrations of TKI until TKI resistant cells dominate the population. (B) The target kinase (e.g., ALK) is amplified via error-prone PCR to generate a spectrum of mutated cDNAs that are transfected or transduced into neoplastic cells, which are then exposed to TKIs to select for TKI- resistant clones. (C) Unbiased genome-wide screens can be conducted to artificially induce or repress expression of genes individually to identify those that can induce resistance. See also Figure 7. 1.6.1 CRISPR-based genome-wide screens CRISPR/Cas9 is a gene-editing platform derived from a microbial adaptive immune system264. Several types of CRISPR/Cas systems have been investigated, but the type II system quickly gained popularity as it required only one protein – Cas9, derived from Streptococcus pyogenes – to achieve CRISPR RNA-guided DNA recognition and cleavage265-266. The first milestone was the discovery that RNA- guided DNA cleavage was only possible when the target DNA sequence was complementary to the CRISPR RNA and possessed a short photospacer adjacent motif (PAM) sequence (5’-NGG for Cas9)265. The second milestone was the discovery that a trans-activating CRISPR RNA (tracrRNA) was essential for CRISPR RNA maturation in the Cas9 system267. The final milestone was the development of a synthetic single guide RNA (sgRNA) that combined the tracrRNA and CRISPR RNA by the addition of a linker loop sequence between the two RNAs265. Soon after CRISPR/Cas9-based loss-of-function (LOF) and gain-of-function (GOF) technologies were developed268–271 (Figure 7). CRISPR/Cas9-based LOF technologies include CRISPR/Cas9 knockout (CRISPR nuclease = CRISPRn) and CRISPR/dCas9-Krüppel272-associated box (KRAB) knockdown (CRISPR interference = CRISPRi) technologies (Figure 7A). For CRISPR/Cas9-based knockout the Cas9–sgRNA complex is targeted to a specific sequence in the coding region of a gene and cleaves both strands of the DNA273,274. The DNA double-strand break is repaired by non-homologous end joining (NHEJ), an error-prone pathway introducing insertion or deletion mutations that can lead to frameshifts275 and a premature termination codon (PTC) in the expressed transcript, resulting in nonsense-mediated decay (NMD) of the mRNA and aberrant peptide products that are degraded276. The CRISPR/dCas9-KRAB knockdown system (Figure 7A) is based on a catalytically dead Cas9 (dCas9), which is generated when Cas9’s two nuclease domains – HNH277 and RuvC-like278 – are mutated264. dCas9 is further fused to a repressor domain such as KRAB. As a result, transcriptional repression is achieved by directly blocking RNA polymerase activity (dCas9) and through effector domain-mediated transcriptional silencing via dCas9-KRAB279,280. The KRAB domain of Kox1, VP16 and p65AD mediates recruitment of Kap1 and HP1 proteins finally leading to transcription silencing through heterochromatin spreading281. library of cDNAs, shRNAs, sgRNAs covering genome-wide CDS DMSO TKI TKI A B C Prolonged exposure to gradually increasing levels of TKI TKI sensitive cells TKI resistant cells TKI sensitive cells transduced cells Mutated target kinase (ALK) cDNAs TKI sensitive cells TKI resistant cells TKI resistant cells Compare Compare 28 Figure 7 Technologies to perturb gene function in mammalian cells for pooled genetic screens (A) LOF technologies include RNA interference (RNAi); CRISPR nuclease (CRISPRn), in which Cas9-mediated DNA cleavage directed to the coding region of a gene by a single guide RNA (sgRNA) results in error-prone repair by cellular non-homologous end joining (NHEJ) pathways, thereby disrupting gene function; and CRISPR interference (CRISPRi), in which catalytically dead Cas9 (dCas9) fused to a transcriptional repressor domain (e.g. KRAB) is recruited to the TSS of an endogenous gene, as specified by an sgRNA, to repress transcription. (B) GOF technologies include overexpression of Open Reading Frames (ORFs) as transgenes; and CRISPR activation (CRISPRa), in which transcriptional activators are recruited via sgRNAs and dCas9 to TSSs of endogenous genes to induce their overexpression. To achieve high levels of overexpression with a single sgRNA, CRISPRa methods recruit more than one transcriptional activator to a given TSS. Multiple activator domains are either directly fused to dCas9 (e.g. VP64, p65 and RTA in the VPR system282), recruited to a protein scaffold fused to dCas9 (e.g. VP64 fused to superfolder GFP (sfGFP) and an antibody single-chain variable fragment (scFv) targeting a GCN4 epitope, which are recruited to a tandem array of 10 copies of the GCN4 epitope in the SunCas system269,283), or recruited to an RNA scaffold fused to the sgRNA (e.g. p65 and HSF1 transcriptional activation domains fused to MS2 coat protein (MCP), dimers of which are recruited to MS2 RNA hairpins in the Synergistic Activation Mediator (SAM) system271. Modified from Kampmann et al.284 shRNA P re -C R IS P R Loss-of-function technologies Gain-of-function technologiesBA RNAi ORF expression Dicer processing siRNA mRNA degradation and translational inhibition R N A s c a ff o ld in g S A M P ro te in s c a ff o ld in g d C a s 9 -S u n T a g D ir e c t fu s io n d C a s 9 -V P R C R IS P R i d C a s 9 -K R A B k n o c k d o w n C R IS P R n C a s 9 k n o c k o u t C R IS P R -b a s e d C R IS P R a sgRNA DNA target Genomic locus Cas9 PAM VP64 sgRNA MS2-P65-HSF1 DNA target Genomic locus dCas9 PAM MS2 hairpins VP64 DNA target Genomic locus dCas9 PAM p65 RTA DNA target Genomic locus dCas9 PAM VP64 sfGFP scFv-GCN4 10xGCN4 sgRNA sgRNA mRNA downregulation KRAB Pol II Transcriptional silencing DNA target Genomic locus dCas9 PAM Heterochromatin spreading Kap1 & HP1 recruitment NHEJ Premature stop codon Indel mutation 29 CRISPR/Cas9-based GOF technologies include CRISPR activation (CRISPRa) systems such as the CRISPR/dCas9-VP64 overexpression (Synergistic Activation Mediator = SAM) system271. Transcriptional activation is achieved by fusion of activator domains such as a tetrameric VP16 (VP64)268 to dCas9, which recruits transcriptional complexes to the TSS of target transcripts as well as an altered sgRNA to recruit accessory transcriptional co-activators (MS2-p65-HSF1) to synergistically interact with the transcriptional complex. Specifically, a hairpin aptamer that selectively binds to the MS2 phage protein was appended to the tetraloop and stem loop no. 2 regions285. Then a separate vector was constructed to express MS2 protein fused to the p65 transcription factor and heat shock transcription factor 1 (HSF1). This allows for additional recruitment of transcriptional activators to the TSS, leading to >500-fold enhanced overexpression in target mRNA levels compared to dCas9-VP64 alone271. The ability to multiplex sgRNAs, and Cas9’s capacity to modulate expression of various endogenous genes, inspired the development of CRISPR screen libraries286. The Genome-Scale CRISPR Knock- Out (GeCKO) library was the first genome-wide CRISPR/Cas9-based KO screen library to be published287. As it was demonstrated to be superior compared to RNAi LOF screens (Figure 7A), specifically in a TKI resistance research setting, as it identified novel resistance mechanisms against the BRAF inhibitor vemurafenib previously not identified with an RNAi screen263, many groups have since used it to identify putative drug resistance mechanisms (Table 9). Table 9 Previously published CRISPRn screens on drug resistance using GeCKO A v2 and B libraries288 Selection Organism Reference Vemurafenib A375 Genome-scale CRISPR-Cas9 knockout screening in human cells263 Etoposide HL60, KBM70 Genetic screens in human cells using the CRISPR-Cas9 system289 Clostridium septicum α-toxin mouse embryonic stem cells Genome-wide recessive genetic screening in mammalian cells with a lentiviral CRISPR-guide RNA library290 Diphtheria toxin HeLa High-throughput screening of a CRISPR/Cas9 library for functional genomics in human cells291 Vemurafenib A375, HEK293T, BV2 Optimized sgRNA design to maximize activity and minimize off- target effects of CRISPR-Cas9292 Ricin K562 Genome-Scale CRISPR-Mediated Control of Gene Repression and Activation269 Sorafenib Huh7 Genome-wide CRISPR screen reveals SGOL1 as a druggable target of sorafenib-treated hepatocellular carcinoma293 Cisplatin SKOV3, A2780 Loss of ZNF587B and SULF1 contributed to cisplatin resistance in ovarian cancer cell lines based on Genome-scale CRISPR/Cas9 screening294 Rigosertib K562 Combined CRISPRi/a-Based Chemical Genetic Screens Reveal that Rigosertib Is a Microtubule-Destabilizing Agent295 30 The Weissman group combined CRISPR/Cas9-based KO with CRISPR/dCas9-based activation screens showing accurate complementarity269. A subsequent study by Feng Zhang’s group identified mediators of resistance to PLX-4720271 by validating CRISPR/dCas9-based activation screens (Table 10). This eventually led to the replacement of Open Reading Frame (ORF) overexpression screen technologies (Figure 7A), as CRISPR/dCas9-based activation screens hold two key advantages: First, ORF libraries do not replicate the transcript isoform variance. In contrast, guiding dCas9-VP64 upstream of the TSS of genes can induce overexpression of several transcript variants with a single sgRNA271. Second, ORF libraries suffer from selection bias as large cDNAs are packaged into lentiviral particles at a much lower efficiency, leading to an overrepresentation of genes with smaller ORFs271,296. In contrast, dCas9-VP64 induces the overexpression of different genes by the 20 bp guide sequence present in the sgRNA. Table 10 Previously published CRISPRa screens on drug resistance Selection Organism Reference Cytarabine MOLM14 An Integrated Genome-wide CRISPRa Approach to Functionalize lncRNAs in Drug Resistance297 Rigosertib K562 Combined CRISPRi/a-Based Chemical Genetic Screens Reveal that Rigosertib Is a Microtubule-Destabilizing Agent295 Vemurafenib A375 Dual direction CRISPR transcriptional regulation screening uncovers gene networks driving drug resistance298 Vemurafenib A375 Genome-scale activation screen identifies a lncRNA locus regulating a gene neighbourhood299 BRAF inhibitor PLX-4720 A375 Genome-scale transcriptional activation by an engineered CRISPR-Cas9 complex271 1.7 Aims of the PhD This PhD aims to: • Determine a bypass resistance landscape to ALK inhibition in ALK+ ALCL • Explore the mechanism by which IL10RA mediates resistance to ALK inhibition in ALK+ ALCL • Generate and characterize PDX models of ALK+ ALCL • Explore if PIM1 overexpression mediates resistance to ALK inhibition in ALK-driven NB/ALK+ ALCL • Establish and validate an assay for the detection of anti-ALK autoantibodies in ALK+ malignancies 31 CHAPTER 2 Materials & Methods 32 2.1 Key Reagents and Resources The reagents or resources (Table 11) and methods described in this chapter of the thesis partly form sections of two separate publications (Trigg, Lee & Prokoph et al.300, Prokoph et al.301), which can be found in Appendix 1. Table 11 Key Reagents and Resources Reagent or Resource Source Identifier Antibodies Anti-GFP antibody Abcam Cat#: ab290 Polyclonal anti-Cas9 antibody Abcam Cat#: ab204448, 1:2000 dilution Goat anti-rabbit immunoglobulin/HRP Agilent technologies Cat#: P0448, 1:10000 dilution Rabbit anti-mouse immunoglobulins/HRP Agilent technologies Cat#: P0161, 1:10000 dilution Rabbit anti-human immunoglobulins/HRP Agilent technologies Cat#: P0214, 1:100 dilution Rabbit ALK (D5F3®) XP® antibody Cell Signalling Cat#: 3633, 1:1000 dilution Rabbit Phospho-ALK (Tyr1604) antibody Cell Signalling Cat#: 3341, 1:1000 dilution Rabbit Phospho-STAT3 (Tyr705) antibody Cell Signalling Cat#: 9145S, 1:1000 dilution Rabbit STAT3 antibody Cell Signalling Cat#: 4904S, 1:1000 dilution Rabbit STAT1 antibody Cell Signalling Cat#: 9172 S, 1:1000 dilution Rabbit Phospho-STAT1 (Tyr701) antibody Cell Signalling Cat#: 14994 S, 1:1000 dilution Rabbit Phospho-STAT5 (Tyr694) antibody Cell Signalling Cat#: 9359 S, 1:1000 dilution Mouse STAT5A (4H1) antibody Cell Signalling Cat#: 4807 S, 1:1000 dilution Bond™ Ready-to-Use Primary Antibody Anaplastic Lymphoma Kinase (5A4) Leica Biosystems Newcastle Ltd Cat#: PA0306 BOND™ Ready-to-Use Primary Antibody CD30 (JCM182) Leica Biosystems Newcastle Ltd Cat#: PA0790 Mouse anti-α-Tubulin Sigma Aldrich Cat#: T9026, 1:10000 dilution Mouse anti-GAPDH Cell Signalling Cat#: 97166S, 1:10000 dilution ALK monoclonal antibody Thermo Fisher Scientific Cat#: 35-4300, 1:1000 dilution Rabbit IL10RA antibody Abcam Cat#: ab94811, 1:100 dilution Rabbit IL10RB antibody Abcam Cat#: ab106282, 1:100 dilution Rabbit IL10 antibody Abcam Cat#: ab34843 1:200 dilution Monoclonal anti-human IgG (Fc specific) antibody Sigma-Aldrich Cat#: I6260 Human IgG Sigma-Aldrich Cat#: I4506 goat anti-human IgG (H&L) – Affinity Pure, DyLight550 Conjugate ImmunoReagents Cat#: GtxMu-003- E2550NHSX anti-GST antibody Abcam Cat#: ab117484 Anti-STAT3 (124H6) antibody Cell Signalling Cat#: 9139 Bacteria and Virus Strains lenti dCAS-VP64_Blast 271, a gift from Feng Zhang RRID: Addgene_61425 33 Reagent or Resource Source Identifier lenti MS2-P65-HSF1_Hygro 271, a gift from Feng Zhang RRID: Addgene_61426 lenti sgRNA(MS2)_zeo backbone 271, a gift from Feng Zhang RRID: Addgene_61427 lenti sgRNA(MS2)_puro backbone 271, a gift from Feng Zhang RRID: Addgene_73795 Human CRISPR Activation Library 271, a gift from Feng Zhang RRID: Addgene_100000005 7 lentiCRISPR v2 288, a gift from Feng Zhang RRID: Addgene_52961 pMD2.G A gift from Didier Trono RRID: Addgene_12259 psPAX2 A gift from Didier Trono RRID: Addgene_12260 pRSV-Rev A gift from Didier Trono RRID: Addgene_12253 pCMVR8.74 A gift from Didier Trono RRID: Addgene_22036 pLKO.1-puro 302, a gift from Prof. Bob Weinberg RRID: Addgene_8453 non-targeting shRNA 303, a gift from David Sabatini RRID: Addgene_1864 pLKO.1 STAT3 shRNA #1 304, a gift from Roberto Chiarle TRCN0000020842 pLKO.1 STAT3 shRNA #2 304, a gift from Roberto Chiarle TRCN0000020840 pLKO.1_PIM1 shRNA #1 300 TRCN0000010118 pLKO.1_PIM1 shRNA #2 300 TRCN0000199011 FgH1tUTG 305, a gift from Marco Herold RRID: Addgene_70183 pHR-SFFV-KRAB-dCas9-P2A-mCherry 269, a gift from Jonathan Weissman RRID: Addgene_60954 pSB700 Cerulean-Zhang2.0 306, a gift from George Church RRID: Addgene_79378 pSB700 Cerulean-Zhang2.0 puro 297 N/A pSB700 Cerulean-Zhang2.0 BFP 297 N/A pSB700 Cerulean-Zhang2.0 RFP 297 N/A pLVUT-tTR-KRAB 307, a gift from Patrick Aebischer & Didier Trono RRID: Addgene_11651 pLVTHM 308, a gift from Didier Trono RRID: Addgene_12247 pLVTHM vector containing the H1 promoter ALK- shRNA (A5) cassette 309 N/A pLX302 310, a gift from David Root RRID: Addgene_25896 pLX302 IL10RA-V5 puro 311, a gift from Kevin Janes RRID: Addgene_47552 pcDNA™ 3.1 vector Invitrogen Cat#: V79020 pcDNA NPM-ALK a kind gift from Steve Morris N/A NEB Stable Competent E. coli (High Efficiency) New England Biolabs (NEB) Cat#: C3040I ElectroMAX Stbl4 Competent Cells Thermo Fisher Cat#: 11635018 Chemicals, Peptides, and Recombinant Proteins T4 PNK NEB Cat#: M0201S 10X T4 Ligation Buffer NEB Cat#: M0541 BsmB1 NEB Cat#: R05805 T4 DNA Ligase NEB Cat#: M0202S Pfu DNA Polymerase Thermo Fisher Scientific Cat#: EP0571 FastDigest® DpnI NEB Cat#: FD1703 34 Reagent or Resource Source Identifier AgeI-HF NEB Cat#: R0552S EcoRI-HF NEB Cat#: R0101S DTT ThermoFisher Cat#: 707265ML SOC Outgrowth Medium NEB Cat#: B9020 TransIT-293 MirusBio Cat#: MIR 2700 Xfect™ Transfection Reagent Takara Bio Cat#: 631318 Lipofectamine® 3000 Transfection Reagent Thermo Fisher Scientific Cat#: L3000001 Zeocin InvivoGen Cat#: Ant-zn-1 Puromycin Gibco Cat#: A1113803 Blasticidin Thermo Fisher Scientific Cat#: R21001 Hygromycin B Thermo Fisher Scientific Cat#: 10687010 Dubbecco’s phosphate buffered saline (DPBS) Sigma-Aldrich Cat#: D8537 Fetal bovine serum (FBS) Labtech Cat#: FCS-SA/500 Dulbecco’s Modified Eagle Medium (DMEM), Hi Gluc, pyruvate Thermo Fisher Scientific Cat#: 41966029 Roswell Park Memorial Institute (RPMI) 1640 Thermo Fisher Scientific Cat#: 21875091 Iscove's Modified Dulbecco's Medium (IMDM) Thermo Fisher Scientific Cat#: 21980032 Opti-MEM® I Reduced Serum Medium Thermo Fisher Scientific Cat#: 51985026 Penicillin/Streptomycin Solution Thermo Fisher Scientific Cat#: 15140122 1 x Trypsin- Ethylenediaminetetraacetic acid (EDTA) Life Technologies Cat#: 25300054 Insulin-transferrin-selenium Thermo Fisher Scientific Cat#: 41400045 Trypan blue Thermo Fisher Scientific Cat#: 15250061 Q5® High-Fidelity DNA Polymerase New England Biolabs Cat#: M0491S Herculase II Fusion DNA Polymerase Agilent Cat#: 600677 APC-Annexin V BioLegend Cat#: 640920 Annexin V Binding Buffer Thermo Fisher Scientific Cat#: V13246 Propidium Iodide (PI) Sigma-Aldrich Cat#: P4170-10MG Power SYBR Green PCR Master Mix ThermoFisher Scientific Cat#: A25741 PierceTM RIPA buffer Thermo Scientific Cat#: 89900 Immobilon Western Chemiluminescent HRP Substrate Merck Millipore Cat#: WBKLS0050 Dimethyl sulfoxide (DMSO) Sigma-Aldrich Cat#: D8418 Crizotinib MedChemExpress Cat#: HY-50878 Alectinib MedChemExpress Cat#: CH5424802 Lorlatinib MedChemExpress Cat#: HY-12215 Ceritinib MedChemExpress Cat#HY-15656 Brigatinib MedChemExpress Cat#: HY-12857 Vinblastine Sigma-Aldrich V1377 AZD1208 AstraZeneca N/A IBL-101 Inflection Biosciences N/A Stattic MedChemExpress Cat#: HY-13818 Interleukin-10 human Sigma-Aldrich Cat#: H7541-10UG PhiX Sequencing Control v3 Illumina Cat#: FC-110-3001 HaltTM Protease Inhibitor Cocktail Thermo Scientific Cat#: 87786 HaltTM Phosphatase Inhibitor Cocktail Thermo Scientific Cat#: 78420 RestoreTM PLUS Western Blot Stripping Buffer Thermo Scientific Cat#: 46430 IDetect Super Stain System – HRP Empire Genomics Cat#: IDST1007 Target Retrieval Solution, Citrate pH6.1 (10X) Agilent Dako Cat#: S2369 Hematoxylin Solution, Gill No. 3 Sigma-Aldrich Cat#: GHS332 3,3’-Diaminobenzidine (DAB) substrate Sigma-Aldrich Cat#: D4293 Aquatex Merck Cat#: 108562 Faramount mounting medium Dako Cat#: S302580 StabilGuard® Choice SurModics Cat#: SG02 DY-633 carboxylic acid carrier dye Dyomics Cat#: 633-00 35 Reagent or Resource Source Identifier ALK protein Thermo Fisher Scientific Cat#: PR7396B Glutathione S-Transferase (GST) GeneScript Cat#: Z02039 RNase A Roche Cat#: 10109169001 DNAse I Thermo Scientific Cat#: 18068015 Proteinase K Thermo Scientific Cat#: EO0491 Dynabeads™ Protein G Thermo Scientific Cat#: 10004D Lymphoprep STEMCELL TECHNOLOGIES Cat#: 07801 cOmplete™ Mini EDTA-free Protease Inhibitor Cocktail Roche Cat#: 11836170001 Matrigel matrix Corning Cat#: 354277 Critical Commercial Assays QIAprep Spin Plasmid Kit Qiagen Cat#: 27104 EndoFree Plasmid Maxi Kit Qiagen Cat#: 12362 RNeasy Plus Mini Kit Qiagen Cat#: 74134 High-Capacity RNA-to-cDNA™ Kit Applied Biosystems Cat#: 4387406 Q5 High-Fidelity PCR Kit NEB Cat#: E0555S CellTiter-Glo Promega Cat#: G7572 CellTiter-Blue® Cell Viability Assay Promega Cat#: G8081 PierceTM BCA Protein Assay Kit Thermo Scientific Cat#: 23225 QIAamp DNA Blood Maxi Kit Qiagen Cat#: 51194 QIAamp DNA Mini Kit Qiagen Cat#: 51304 Q5 High-Fidelity PCR Kit NEB Cat#: E0555S Zymo DNA Clean and Concentrator-5 Zymo research Cat#: D4003 KAPA Library Quantification Kit Kapa Biosystems Cat#: Q32850 TruSeq Stranded mRNA kit Illumina Cat#: 20020595 AEC Substrate Kit BDPharmingen Cat#: 551015 Avidin/Biotin Blocking Kit Vector Laboratories Cat#: SP2001 QIAquick PCR Purification Kit Qiagen Cat#: 28106 AMIDot™ Activation Diluent Cambridge Life Sciences Cat#: N7214D AUTOZYME™ RF Wash Buffer Cambridge Life Sciences Cat#: N7206D AUTOZYME™ RF Sample Diluent Cambridge Life Sciences Cat#: N7015D Deposited Data CRISPR overexpression screens sgRNA Counts in ALCL cells treated with DMSO or crizotinib (Prokoph et al.)301 Table S9 CRISPR knockout screens sgRNA Counts in ALCL cells treated with DMSO or crizotinib (Prokoph et al.)301 Table S10 CRISPR overexpression screens sgRNA Counts in NB cells treated with DMSO, brigatinib or ceritinib (Trigg, Lee & Prokoph et al.)300 Supplementary Data 1, Supplementary Data 2 CRISPR knockout screens sgRNA Counts in ALCL cells 312 Supplementary Data 5 Supplementary Data 6 RNA-seq from 2 ALK inhibitor resistant and 2 chemotherapy relapsed ALK+ ALCL patients (Prokoph et al.)301 N/A STAT3 ChIP-seq for CD4+ T-cells 313 GEO: GSE21669 STAT3 ChIP-seq for ALCL cell lines 304 GEO: GSE117164 Expression profiling by array from 23 ALK+ ALCL patients, 12 reactive lymph nodes 314 GEO: GSE78513 Expression profiling by array from 67 AITL, 73 PTCL- NOS, 17 ALK+ and 18 ALK- ALCL patients; as well as T cells from 9 healthy individuals. 315 GEO: GSE58445 Expression profiling by array from 6 AITL, 28 PTCL- NOS, 2 ALK+ and 4 ALK- ALCL patients; as well as T cells from 10 healthy individuals. 316 GEO: GSE6338 36 Reagent or Resource Source Identifier Expression profiling by array from 5 ALK+ and 4 ALK- ALCL patients, 23 T cells and 3 ALK+ ALCL cell lines. 317 GEO: GSE14879 Expression profiling by array from 36 AITL, 45 PTCL- NOS, 20 ALK+, 9 ALK- ALCL patients; as well as 10 T cells and 3 T-cell control cell lines. 318 GEO: GSE19069 Expression profiling by array from 8 PTCL-NOS, 10 ALK+ and 13 ALK- ALCL patients 319 GEO: GSE65823 Expression profiling by array from 61 lymphoma cell lines 320 GEO: GSE94669 Expression profiling by array from 4 ALK+, 2 ALK- and 4 T-cell control cell lines 321 GEO: GSE107951 Expression profiling by array from doxycycline induced or non-induced TS and SU-DHL-1 cells. Expression profiling by array from ALK inhibitor treated TS cells. 322 GEO: GSE6184 Experimental Models: Cell Lines Human: DEL (Male, 12 years) Deutsche Sammlung von Mikroorganismen und Zellkulturen (DSMZ) Cat#: ACC 338, RRID: CVCL_1170 Human: KARPAS-299 (Male, 25 years) European Collection of Authenticated Cell Cultures (ECACC) Cat#: 06072604, RRID: CVCL_1324 Human: SU-DHL-1 (Male, 10 years) DSMZ Cat#: ACC 356, RRID: CVCL_0538 Human: SUP-M2 (Female, 5 years) DSMZ Cat#: ACC 509, RRID: CVCL_2209 Human: COST (Male, 4 years) a gift from Roberto Chiarle RRID: CVCL_9491 Human: TS (Female, 5 years) 323, a gift from Roberto Chiarle RRID: CVCL_B228 Human: JB6 (Male, 12 years) a gift from Roberto Chiarle RRID: CVCL_H633 Human: ManGoSteen = MGS (Female, 7 years) (Prokoph & Matthews et al., unpublished) RRID: N/A Human: MaToKe = MTK (Male, 5 years) (Prokoph & Matthews et al., unpublished) RRID: N/A Human: Mac-2A (Male, 47 years) 324, a gift from Olaf Merkel RRID: CVCL_H637 Human: FE-PD (Female, 46 years) DSMZ RRID: CVCL_H614 Human: CHLA-20 (Female, 2 years) COG RRID: CVCL_6602 Human: CHLA-90 (Male, 6 years) COG RRID: CVCL_6610 Human: CHLA-95 (Female) COG RRID: CVCL_6611 Human: CHLA-171 (Male, 8 years) COG RRID: CVCL_6597 Human: COG-N-426 (Felix-CL, Male) COG RRID: CVCL_LF58 Human: GI-ME-N (Female, 3 years) DSMZ Cat#: ACC654; RRID: CVCL_1232 Human: KELLY (Female, 1 year) ECACC Cat#: 92110411; RRID: CVCL_2092 Human: LAN-5 (Male, 4 months) COG RRID: CVCL_0389 Human: LAN-6 (Male, 6 years) COG RRID: CVCL_1363 Human: NB-1643 (Male, 3 years) COG RRID: CVCL_5627 Human: NBL-S (Male, 3 years) DSMZ Cat#: ACC656; RRID: CVCL_2136 Human: NGP (Male, 2 years) DSMZ Cat#: ACC676; RRID: CVCL_2141 Human: SH-SY5Y (Female, 4 years) ECACC Cat#: 94030304; RRID: CVCL_0019 Human: LM1 (Female, 13 years) 325 N/A Human: HEK293T (Female) Invitrogen Cat#: R70007, RRID: CVCL_6911 Monkey: COS-1 (Male) DSMZ Cat#: ACC63, RRID: CVCL_0223 Human: DEL CR (Male, 12 years) (Prokoph et al.)301 N/A Human: DEL AR (Male, 12 years) (Prokoph et al.)301 N/A 37 Reagent or Resource Source Identifier Human: KARPAS-299 CR (Male, 25 years) 217, a gift from Luca Mologni RRID: CVCL_V404 Human: KARPAS-299 AR (Male, 25 years) 326, a gift from Luca Mologni N/A Human: KARPAS-299 BR (Male, 25 years) 327, a gift from Luca Mologni N/A Human: KARPAS-299 LR (Male, 25 years) 328, a gift from Luca Mologni N/A Human: SU-DHL-1 CR (Male, 10 years) (Prokoph et al.)301 N/A Human: SU-DHL-1 AR (Male, 10 years) (Prokoph et al.)301 N/A Human: SUP-M2 CR (Female, 5 years) 217, a gift from Luca Mologni RRID: CVCL_V405 Human: SUP-M2 AR (Female, 5 years) (Prokoph et al.)301 N/A Human: SUP-M2 BR (Female, 5 years) 327 N/A Human: SUP-M2 LR (Female, 5 years) 328, a gift from Luca Mologni N/A Human: stable doxycycline inducible NPM1-ALK shRNA TS (Female, 5 years) 323, a gift from Roberto Chiarle N/A Human: stable doxycycline inducible NPM1-ALK shRNA SU-DHL-1 (Male, 10 years) 323, a gift from Roberto Chiarle N/A Experimental Models: Organisms/Strains C.B.17/IcrHanHsd-Prkdc (Mus musculus) Envigo Cat#: 88304F NOD.Cg-Prkdcscid Il2rgtm1WjI/SzJ (Mus musculus) Charles River Cat#005557; RRID: IMSR_ARC:NSG MGS-A-x (Prokoph & Matthews et al., unpublished) RRID: N/A Software and Algorithms FlowJo 10 Treestar https://www.flowjo.co m/ R Statistical Software 3.6 N/A https://www.r- project.org/ Inkscape 0.92.4 N/A https://inkscape.org/ MAGeCK 329 https://sourceforge.ne t/p/mageck/ wiki/Home/ MAGeCK-VISPR 329 http://bitbucket.org/liul ab/mageck-vispr Salmon 330 https://github.com/CO MBINE-lab/salmon tximport 331 https://bioconductor.o rg/packages/release/ bioc/html/tximport.htm l edgeR 332 https://bioconductor.o rg/packages/release/ bioc/html/edgeR.html DESeq2 333 http://bioconductor.or g/packages/ release/bioc/html/DE Seq2.html biomaRt 334 http://bioconductor.or g/packages/ release/bioc/html/bio maRt.html fgsea 335 http://bioconductor. org/packages/release /bioc/html/ fgsea.html topGO 336 https://bioconductor.o rg/packages/release/ bioc/html/topGO.html 38 Reagent or Resource Source Identifier SAM Cas9 activator tool Zhang lab http://sam.genome- engineering.org/datab ase/ CRAN: survminer 337 https://cran.r- project.org/web/ packages/survminer/i ndex.html CRAN: survival package in R 338 https://cran.r- project.org/web/ packages/survival/ind ex.html GraphPad Prism 8.2 GraphPad www.graphpad.com SRA toolkit National Center for Biotechnology Information (NCBI) https://github.com/ncb i/sra-tools SAMtools 339 http://samtools.source forge.net/ Bowtie2 340 http://bowtie- bio.sourceforge.net/b owtie2/index.shtml BEDTools 341 https://sourceforge.ne t/projects/bedtools/ IGV 342,343 http://software.broadi nstitute.org/software/i gv/ GEOquery 344 https://bioconductor.o rg/packages/release/ bioc/html/GEOquery. html oligo 345 https://www.biocondu ctor.org/packages/rel ease/bioc/html/oligo.h tml arrayQualityMetrics 346 https://bioconductor.o rg/packages/release/ bioc/html/arrayQuality Metrics.html AnnotationDbi 347 https://bioconductor.o rg/packages/release/ bioc/html/AnnotationD bi.html limma 348 https://bioconductor.o rg/packages/release/ bioc/html/limma.html beadarray 349 https://www.biocondu ctor.org/packages/rel ease/bioc/html/beada rray.html illuminaHumanv4.db 350 http://bioconductor.or g/packages/release/d ata/annotation/html/ill uminaHumanv4.db.ht ml Human Reference Genome (GRCh38.p12) Genome Reference Consortium https://www.ncbi.nlm. nih.gov/assembly/GC F_000001405.38 GENCODE transcriptome annotation version v29 351 https://www.gencode genes.org/human/rele ase_29.html Human Reference Genome (hg19) Genome Reference Consortium https://www.ncbi.nlm. nih.gov/grc/human Mouse Reference Genome (mm10) Genome Reference Consortium https://www.ncbi.nlm. nih.gov/grc/mouse KEGG: Kyoto Encyclopedia of Genes and Genomes 352 https://ww.genome.jp/ kegg 39 Reagent or Resource Source Identifier PrimerBLAST NCBI https://www.ncbi.nlm. nih.gov/tools/primer- blast/ PrimerBank 353 https://pga.mgh.harva rd.edu/primerbank/ Other FACSAria™ Fusion 2 flow cytometer BD Bioscience N/A 2100 BioAnalyzer System Agilent N/A Illumina NextSeq500 Illumina N/A QuantStudio™ 6 Flex Real-Time PCR System ThermoFisher N/A Accuri C6 flow cytometer BD Bioscience N/A ABI Veriti Thermal Cycler Applied Biosystems N/A 30-gauge hypodermic AganiTM needle VWR Cat#: 613-5373 Plastic feeding tubes (22ga (black) x 38mm) Instech Laboratories Cat#: FTP-22-38 70 M nylon Falcon™ cell strainer Thermo Fisher Scientific Cat#: 352350 5 mL Plastipak™ syringe Thermo Fisher Scientific Cat#: 302187 Minisart filters (0.45 μm pore size) Merck Millipore Cat#: 16555K Minisart filters (0.2 μm pore size) Sartorius Stedim Biotech Cat#: 17823K Vivaspin centrifugal filter column with a molecular weight cut-off of 3 kDa Sartorius Stedim Biotech Cat#: VS0191 SpectraMax i3 Molecular Devices N/A Mini-Protean TGX Precast Gels Bio-Rad Cat#: 4561033 Trans-Blot Turbo Transfer Pack Bio-Rad Cat#: 1704156 Trans-Blot Turbo Transfer System Bio-Rad N/A Gene Pulser II Electroporation System Bio-Rad N/A Flat-bottom 96-well plates Corning Cat#: 167008 96 well V-bottom plates Greiner bio-one Cat#: 651201 Black 96-well Cellstar plate Greiner bio-one Cat#: 655090 2D-Epoxy functionalized glass slide PolyAN Molecular Surface Engineering Cat#: 10400221 3D-Epoxy functionalized glass slide PolyAN Molecular Surface Engineering Cat#: 10400205 3D-NHS functionalized glass slide PolyAN Molecular Surface Engineering Cat#: 10400405 2D-Aldehyde functionalized glass slide PolyAN Molecular Surface Engineering Cat#: 10400325 3D-Aldehyde functionalized glass slide PolyAN Molecular Surface Engineering Cat#: 10400305 Dako Pen Dako Cat#: S2002 Superfrost™ microscope slides Thermo Fisher Scientific Cat#: 10143560W90 Cytocentrifuge Shandon N/A Automated slide processor ZenitUP A. Menarini Diagnostics N/A Port Array 5000TM Aurora Photonics N/A Nano-PlotterTM NP2.1 GeSiM N/A AIRWIN ultrasonic humidifier BOGA Gerätetechnik N/A programmable temperature controller PolyScience N/A Bioruptor® Pico Diagenode N/A Nanodrop 1000 ThermoFisher N/A LAS-4000 Image Analyzer Fujifilm/Raytek N/A Water-sensitive paper Quantifoil Instruments Cat#: 3100-0011 15-mL polypropylene centrifuge tubes Sarstedt Cat#: CEN255 Sonication beads Diagenode Cat#:C03070001 High sensitivity D1000 ScreenTape Agilent N/A 40 2.2 Patient samples Patient samples utilized in this thesis include samples collected from patients enrolled to clinical trials (Table 12). Table 12 Clinical trials from which samples have been acquired (*) as stated on the ClinicalTrials.gov webpage. NIH study trial registration no. Description Study timeframe* Disease Patients Experiment purpose NCT00006455 ALCL99: Combination chemo- therapy in treating children with ALCL Dec 1999 – N/A ALCL 42 TMA N/A NHL-BFM95 N/A ALCL 24 TMA N/A NHL-BFM90 N/A ALCL 26 TMA NCT02613962 MAPPYACTS: Proof -of - concept study to stratify targeted therapies adapted to molecular profiling Dec 2005 – Dec 2020 ALCL 4 RNA-seq 2.2.1 NHL-BFM90 trial cohort The BFM group study NHL-BFM9033 enrolled paediatric patients with B or T-cell NHL, including paediatric ALCL patients, to test increasing doses of methotrexate. From this study, archival paraffin- embedded tissue blocks were used to create tissue microarrays (TMAs) containing cores selected from representative tumour areas as determined by a consultant histopathologist from hematoxylin and eosin–stained sections. FFPE tissue specimens from individuals with ALK+ ALCL treated in the NHL-BFM9033 trial were obtained from both male (n = 19) and female (n = 7) peadiatric subjects (Table 13) with informed consent and in accordance with the Declaration of Helsinki. The study was approved by the institutional ethics committee of the primary investigator of the NHL-BFM study group. Patients with completely resected Stage 1 disease were treated with different chemotherapy regimens and as such were excluded from this study. Eligibility was confirmed by demonstration of NPM1-ALK positivity of the tumour either by NPM1-ALK polymerase chain reaction, two-color fluorescence in situ hybridization for the t(2;5) or nuclear and cytoplasmic staining for ALK. Staging procedures included bone marrow aspiration cytology and a spinal tap. Bone marrow involvement was defined by cytologically detectable ALCL cells, irrespective of their number. The patient’s treatment consisted of a cytoreductive prephase followed by six chemotherapy courses, as previously described33. 41 Table 13 Clinical Information of Paediatric ALCL Patients Recruited onto the NHL-BFM90 Trial Case no. Sex Age group (in years) Diagnosis Study Relapse Death 1 F >= 10 ALCL, ALK+ NHL-BFM90 no no 2 M < 10 ALCL, ALK+ NHL-BFM90 no no 3 M < 10 ALCL, ALK+ NHL-BFM90 no no 4 M < 10 ALCL, ALK+ NHL-BFM90 no no 5 M >= 10 ALCL, ALK+ NHL-BFM90 no no 6 M >= 10 ALCL, ALK+ NHL-BFM90 no no 7 F >= 10 ALCL, ALK+ NHL-BFM90 no no 8 F >= 10 ALCL, ALK+ NHL-BFM90 no no 9 M < 10 ALCL, ALK+ NHL-BFM90 no no 10 M >= 10 ALCL, ALK+ NHL-BFM90 no no 11 M >= 10 ALCL, ALK+ NHL-BFM90 no no 12 F >= 10 ALCL, ALK+ NHL-BFM90 no no 13 M < 10 ALCL, ALK+ NHL-BFM90 no no 14 M < 10 ALCL, ALK+ NHL-BFM90 no no 15 M < 10 ALCL, ALK+ NHL-BFM90 no no 16 F >= 10 ALCL, ALK+ NHL-BFM90 no no 17 M < 10 ALCL, ALK+ NHL-BFM90 no no 18 F < 10 ALCL, ALK+ NHL-BFM90 yes yes 19 M >= 10 ALCL, ALK+ NHL-BFM90 no no 20 M < 10 ALCL, ALK+ NHL-BFM90 no no 21 M >= 10 ALCL, ALK+ NHL-BFM90 yes no 22 F < 10 ALCL, ALK+ NHL-BFM90 yes no 23 M < 10 ALCL, ALK+ NHL-BFM90 no no 24 M >= 10 ALCL, ALK+ NHL-BFM90 yes no 25 M >= 10 ALCL, ALK+ NHL-BFM90 no no 26 M < 10 ALCL, ALK+ NHL-BFM90 yes no 2.2.2 NHL-BFM95 trial cohort Given the high risk of short-term side effects associated with methotrexate51, lower concentrations of methotrexate administered in shorter pulses were applied in a subsequent BFM group study, NHL- BFM9594. From this study, archival paraffin-embedded tissue blocks were used to create TMAs containing cores selected from representative tumour areas as determined by a consultant histopathologist, from hematoxylin and eosin–stained sections. FFPE tissue specimens from individuals with ALK+ ALCL treated in the NHL-BFM9594 trial were obtained from both male (n = 20) and female (n = 4) paediatric subjects (Table 14) with informed consent and in accordance with the Declaration of Helsinki. The study was approved by the institutional ethics committee of the primary investigator of the NHL-BFM study group. Patients with completely resected Stage 1 disease were treated with different chemotherapy-regimens and as such were excluded from this study. Eligibility was confirmed by demonstration of NPM1-ALK positivity of the tumour either by NPM1-ALK polymerase chain reaction, two-color fluorescence in situ hybridization for the t(2;5) or nuclear and cytoplasmic staining for ALK. Staging procedures included bone marrow aspiration cytology and a spinal tap. Bone marrow involvement was defined by cytologically detectable ALCL cells, irrespective of their number. The patient’s treatment consisted of a cytoreductive prephase followed by six chemotherapy courses, as previously described33. 42 Table 14 Clinical Information of Paediatric ALCL Patients Recruited onto the NHL-BFM95 Trial Case no. Sex Age group (in years) Diagnosis Study Relapse Death 1 M >= 10 ALCL, ALK+ NHL-BFM95 no no 2 M < 10 ALCL, ALK+ NHL-BFM95 no no 3 M >= 10 ALCL, ALK+ NHL-BFM95 no no 4 M < 10 ALCL, ALK+ NHL-BFM95 yes no 5 M >= 10 ALCL, ALK+ NHL-BFM95 no no 6 M < 10 ALCL, ALK+ NHL-BFM95 no no 7 F >= 10 ALCL, ALK+ NHL-BFM95 no no 8 M < 10 ALCL, ALK+ NHL-BFM95 yes no 9 M < 10 ALCL, ALK+ NHL-BFM95 yes no 10 M >= 10 ALCL, ALK+ NHL-BFM95 no no 11 M < 10 ALCL, ALK+ NHL-BFM95 yes yes 12 M >= 10 ALCL, ALK+ NHL-BFM95 no no 13 M >= 10 ALCL, ALK+ NHL-BFM95 yes no 14 M >= 10 ALCL, ALK+ NHL-BFM95 no no 15 M < 10 ALCL, ALK+ NHL-BFM95 no no 16 M >= 10 ALCL, ALK+ NHL-BFM95 no no 17 F < 10 ALCL, ALK+ NHL-BFM95 yes yes 18 M < 10 ALCL, ALK+ NHL-BFM95 no yes 19 M < 10 ALCL, ALK+ NHL-BFM95 no no 20 M >= 10 ALCL, ALK+ NHL-BFM95 yes no 21 M < 10 ALCL, ALK+ NHL-BFMV95 yes no 22 F >= 10 ALCL, ALK+ NHL-BFMV95 no no 23 F < 10 ALCL, ALK+ NHL-BFMV95 yes no 24 M >= 10 ALCL, ALK+ NHL-BFMV95 no no 2.2.3 ALCL99 trial cohort The European intergroup trial ALCL9928 (NCT00006455) is a randomized phase III trial that is studying several different regimens of combination chemotherapy to compare how well they work in treating children with ALCL. The study was approved by the institutional ethics committee of the primary investigator of the NHL-BFM study group. Patients with completely resected Stage 1 disease were treated with different chemotherapy-regimens and as such were excluded from this study. Eligibility was confirmed by demonstration of NPM1-ALK positivity of the tumour either by NPM1-ALK polymerase chain reaction, two-color fluorescence in situ hybridization for the t(2;5) or nuclear and cytoplasmic staining for ALK. Staging procedures included bone marrow aspiration cytology and a spinal tap. Bone marrow involvement was defined by cytologically detectable ALCL cells, irrespective of their number. The patient’s treatment consisted of a cytoreductive prephase followed by six chemotherapy courses, as previously described33. From this study, archival paraffin-embedded tissue blocks were used to create TMAs containing cores selected from representative tumour areas as determined by a consultant histopathologist, from hematoxylin and eosin–stained sections. FFPE tissue specimens from individuals with ALK+ ALCL treated in the ALCL99 trial were obtained from both male (n = 28) and female (n = 14) paediatric subjects (Table 15) with informed consent and in accordance with the Declaration of Helsinki. In addition, serum or plasma samples from individuals with ALK+ ALCL treated in the ALCL99 trial were obtained from both male (n = 53) and female (n = 40) paediatric subjects (Table 40) with informed consent and in accordance with the Declaration of Helsinki. 43 Table 15 Clinical Information of Paediatric ALCL Patients Recruited onto the ALCL99 Trial that provided FFPE tissue specimens Case no. Sex Age group (in years) Diagnosis Study Relapse Death 1 M >= 10 ALCL, ALK+ ALCL99 yes no 2 M >= 10 ALCL, ALK+ ALCL99 yes no 3 F >= 10 ALCL, ALK+ ALCL99 no no 4 M >= 10 ALCL, ALK+ ALCL99 no no 5 M < 10 ALCL, ALK+ ALCL99 yes no 6 M >= 10 ALCL, ALK+ ALCL99 yes no 7 F >= 10 ALCL, ALK+ ALCL99 no no 8 M >= 10 ALCL, ALK+ ALCL99 no no 9 M >= 10 ALCL, ALK+ ALCL99 no no 10 M < 10 ALCL, ALK+ ALCL99 yes no 11 M >= 10 ALCL, ALK+ ALCL99 no no 12 M >= 10 ALCL, ALK+ ALCL99 yes no 13 M >= 10 ALCL, ALK+ ALCL99 no no 14 M >= 10 ALCL, ALK+ ALCL99 no no 15 M >= 10 ALCL, ALK+ ALCL99 yes no 16 F >= 10 ALCL, ALK+ ALCL99 yes no 17 F >= 10 ALCL, ALK+ ALCL99 no no 18 M >= 10 ALCL, ALK+ ALCL99 no no 19 M < 10 ALCL, ALK+ ALCL99 yes no 20 M >= 10 ALCL, ALK+ ALCL99 no no 21 M >= 10 ALCL, ALK+ ALCL99 yes yes 22 M >= 10 ALCL, ALK+ ALCL99 no no 23 F < 10 ALCL, ALK+ ALCL99 no no 24 F >= 10 ALCL, ALK+ ALCL99 no no 25 F >= 10 ALCL, ALK+ ALCL99 no no 26 M >= 10 ALCL, ALK+ ALCL99 no no 27 F >= 10 ALCL, ALK+ ALCL99 no no 28 M < 10 ALCL, ALK+ ALCL99 no no 29 F >= 10 ALCL, ALK+ ALCL99 no no 30 M >= 10 ALCL, ALK+ ALCL99 no no 31 M < 10 ALCL, ALK+ ALCL99 no no 32 M >= 10 ALCL, ALK+ ALCL99 yes no 33 F >= 10 ALCL, ALK+ ALCL99 yes yes 34 F >= 10 ALCL, ALK+ ALCL99 no yes 35 F >= 10 ALCL, ALK+ ALCL99 yes no 36 M >= 10 ALCL, ALK+ ALCL99 no no 37 F < 10 ALCL, ALK+ ALCL99 no no 38 M >= 10 ALCL, ALK+ ALCL99 no yes 39 F >= 10 ALCL, ALK+ ALCL99 yes yes 40 M >= 10 ALCL, ALK+ ALCL99 yes no 41 M < 10 ALCL, ALK+ ALCL99 no no 42 M < 10 ALCL, ALK+ ALCL99 yes no 2.2.4 UK cohort FFPE tissues, bone marrow, peripheral blood and related clinical information from a female (n = 1) paediatric patient with ALK+ ALCL (Table 16) were obtained after written informed parental consent and according to the Declaration of Helsinki. The study was approved by the Huntington research ethics committee (no. 07/Q0104/16). 44 Table 16 Clinical Information of the Paediatric ALCL Patient from the UK Cohort ALCL99 = cyclophosphamide, methotrexate, ifosfamide, etoposide, cytarabine, doxorubicin; CYVE354 = cytarabine, etoposide; N/A = not applicable; VBL = vinblastine. Characteristics Patient 1 Patient 2 Sex F M Age at diagnosis 6 years 5 years Stage at diagnosis 4 4 CNS involvement at diagnosis No No CNS involvement at chemotherapy relapse/refractoriness Yes No CNS involvement at crizotinib relapse/refractoriness Yes Yes 1st-line treatment ALCL99 ALCL99 2nd-line treatment VBL CYVE 3rd-line treatment VBL + iv cytarabine/etoposide with intrathecal hydrocortisone/methotrexate/cytarabine Crizotinib + intrathecal chemotherapy 4th-line treatment VBL + Crizotinib + intrathecal hydrocortisone / methotrexate / cytarabine CYVE 5th-line treatment SCT Vinblastine + steroids 6th-line treatment Crizotinib N/A 7th-line treatment Brentuximab vedotin + VBL N/A Samples used for engraftment into NSG mice Bone marrow taken after 7th treatment line Pleural effusion taken before 3rd treatment line 2.2.5 MAPPYACTS trial cohort Molecular Profiling for Pediatric and Young Adult Cancer Treatment Stratification (MAPPYACTS, NCT02613962) is a prospective, international, multicentric clinical proof-of-concept study to stratify targeted therapies adapted to molecular profiling of relapsed or refractory paediatric tumours. Molecular screening is carried out on newly biopsied or resected tumour samples obtained at the time of relapse/progression, using high-throughput technologies, primarily WES and RNA Sequencing. Clinical and molecular data of samples from individuals with ALK+ ALCL treated with crizotinib (n = 2) or combination chemotherapy (n = 2) were obtained from both male (n = 3) and female (n = 1) pediatric subjects included in the MAPPYACTS trial (Table 17) with informed consent. The MAPPYACTS trial protocol, amendments and informed consent were approved by the ethics committee and complied with local regulations and the Declaration of Helsinki (no. 2015-A00464-45). Table 17 Clinical Information of Paediatric ALCL Patients Recruited onto the MAPPYACTS Trial ALCL99 = cyclophosphamide, methotrexate, ifosfamide, etoposide, cytarabine, doxorubicin; ALCL99* = patient was treated according to ALCL99 recommendations for patients with CNS involvement as specified in Williams et al., 201399; BV = Brentuximab vedotin; CR = Crizotinib; LR = Lorlatinib; Nivo = Nivolumab; VBL = vinblastine. Biopsy sample for RNA-seq was taken at relapse during or after the highlighted treatment line. Characteristics Patient 1 Patient 2 Patient 3 Patient 4 Sex M M F M Age at diagnosis 16 16 10 11 ALK fusion partner NPM1-ALK+ NPM1-ALK+ TFG-ALK+ NPM1-ALK+ 1st-line treatment ALCL99 ALCL99* ALCL99 ALCL99 2nd-line treatment VBL CR CR CR 3rd-line treatment CR Nivo N/A N/A 4th line treatment BV N/A N/A N/A 5th line treatment LR N/A N/A N/A 6th line treatment Nivo N/A N/A N/A CR resistant (R) R R N/A N/A ALK mutation status L1196M not detected not detected not detected 45 2.2.6 Brno cohort FFPE tissue specimens from individuals with angioimmunoblastic T-cell lymphoma (AITL, n = 3), peripheral T-cell lymphoma not otherwise specified (PTCL-NOS, n = 4), ALK+ (n = 16) or ALK- (n = 6) ALCL were obtained from both male (n = 19) and female (n = 10) adult subjects (Table 18) with informed consent and in accordance with the Declaration of Helsinki. The study was approved by the ethics committee of the University Hospital of Brno, Czech Republic (no. 4-306/13/1). From this cohort, archival paraffin-embedded tissue blocks were used to create TMAs containing cores selected from representative tumour areas as determined by a consultant histopathologist, from hematoxylin and eosin–stained sections. Table 18 Clinical Information of T-cell Lymphoma Patients from the Brno Cohort Angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphomas not otherwise specified (PTCL-NOS). Case no. Sex Age at diagnosis Diagnosis Biopsy (Tissue origin) Relapse Death 1 M 21 ALCL, ALK+ lymph node no no 2 M 46 ALCL, ALK+ infiltrate, subhepatal no no 3 M 58 ALCL, ALK+ lymph node, axilla no no 4 M 28 ALCL, ALK+ tumour, axilla no no 5 M 28 ALCL, ALK+ lymp node yes yes 6 F 30 ALCL, ALK+ lymph node, neck no no 7 F 66 ALCL, ALK- skin yes no 8 F 81 ALCL, ALK- lymph node, neck no yes 9 M 71 ALCL, ALK- infiltrate, inguina no yes 10 M 61 ALCL, ALK- nasopharynx no yes 11 M 60 ALCL, ALK- lymph nodes, inguina no no 12 M 57 ALCL, ALK- lymph node, supraclavicula no no 13 F 54 ALCL, ALK- lymph node yes no 14 M 51 ALCL, ALK- lymph node, inguina no no 15 M 89 ALCL, ALK- mandibula yes yes 16 M 51 ALCL, ALK- lymph node, retroperitoneum no yes 17 M 56 ALCL, ALK- lymph node, inguina no no 18 F 44 ALCL, ALK- mesenterium and retroperitoneum with pancreas no yes 19 F 60 ALCL, ALK- lymph node, supraclavicula no no 20 M 57 ALCL, ALK- lymph node no no 21 M 66 ALCL, ALK- lymph node, neck no yes 22 M 61 ALCL, ALK- lymph node, neck no yes 23 F 57 AITL lymph node, neck yes yes 24 M 62 AITL lymph node, axilla yes yes 25 F 60 AITL N/A yes yes 26 F 76 PTCL-NOS lymph node,mesenteric no yes 27 M 77 PTCL-NOS lymph node, neck no no 28 M 80 PTCL-NOS lymph node, groin yes yes 29 F 70 PTCL-NOS lymph node, neck yes yes 46 2.2.7 Pakistan cohort FFPE tissue specimens from individuals with ALK+ (n = 15) or ALK- (n = 9) ALCL were obtained from both male (n = 19) and female (n = 10) subjects with informed consent and in accordance with the Declaration of Helsinki. The study was approved by the ethics committee of the Huntington research ethics committee (no. 07/Q0104/16). From this cohort, archival paraffin-embedded tissue blocks were used to create TMAs containing cores selected from representative tumour areas as determined by a consultant histopathologist, from hematoxylin and eosin–stained sections. Table 19 Clinical Information of T-cell Lymphoma Patients from the Pakistan Cohort Case no. Sex Age at diagnosis Diagnosis Biopsy (Tissue origin) 1 M 57 ALCL, ALK+ inguinal lymph node 2 M 19 ALCL, ALK+ cervical lymph node 3 F 17 ALCL, ALK+ cervical lymph node 4 M 53 ALCL, ALK+ cervical lymph node 5 M 19 ALCL, ALK+ cervical lymph node 6 M 12 ALCL, ALK+ buccal mucosa 7 M 35 ALCL, ALK+ abdominal mass 8 M 38 ALCL, ALK+ mass right axilla 9 M 12 ALCL, ALK+ paravertebral soft tissue 10 M 2 ALCL, ALK+ cervical lymph node 11 M 59 ALCL, ALK+ groin mass 12 F 20 ALCL, ALK+ right side neck swelling 13 F 61 ALCL, ALK+ mesenteric lymph node 14 M 35 ALCL, ALK+ cervical lymph node 15 M 25 ALCL, ALK+ mesenteric nodule & nodulae anterior part of stomach 16 F 27 ALCL, ALK- supraclavicular lymph node 17 M 38 ALCL, ALK- mesenteric lymph node 18 M 67 ALCL, ALK- left arm swelling 19 M 35 ALCL, ALK- cervical lymph node 20 M 75 ALCL, ALK- right supraclavicular 21 M 21 ALCL, ALK- skin lesion - thigh 22 F 20 ALCL, ALK- axillary lymph node 23 M 60 ALCL, ALK- cervical lymph node 24 M 14 ALCL, ALK- inguinal lymph node 2.2.8 Vienna cohort FFPE tissue specimens from individuals with AITL (n = 4) or PTCL-NOS (n = 17) were obtained from both male (n = 9) and female (n = 12) adult subjects (Table 20) with informed consent and in accordance with the Declaration of Helsinki. The study was approved by the ethics committee of the Medical University of Vienna, Austria (no. 1437/2016). From this cohort, archival paraffin-embedded tissue blocks were used to create TMAs containing cores selected from representative tumour areas as determined by a consultant histopathologist from hematoxylin and eosin–stained sections. 47 Table 20 Clinical Information of T-cell Lymphoma Patients from the Vienna Cohort Angioimmunoblastic T-cell lymphoma (AITL), peripheral T-cell lymphomas not otherwise specified (PTCL-NOS). Case no. Sex Age at diagnosis Diagnosis Biopsy (Tissue origin) 1 M 54 AITL inguinal lymph node 2 F 85 AITL lymph node 3 M 58 AITL cervical lymph node 4 F 82 AITL inguinal lymph node 5 F 64 PTCL-NOS axillary lymph node 6 F 70 PTCL-NOS lymph node 7 M 67 PTCL-NOS cervical lymph node 8 F 62 PTCL-NOS peritoneum 9 M 58 PTCL-NOS inguinal lymph node 10 M 54 PTCL-NOS supraclavicular lymph node 11 F 73 PTCL-NOS skin 12 F 87 PTCL-NOS skin 13 F 89 PTCL-NOS skin 14 M 76 PTCL-NOS preauricular lymph node 15 M 63 PTCL-NOS axillary lymph node 16 M 43 PTCL-NOS inguinal lymph node 17 F 78 PTCL-NOS inguinal lymph node 18 F 77 PTCL-NOS axillary lymph node 19 F 65 PTCL-NOS inguinal lymph node 20 M 55 PTCL-NOS inguinal lymph node 21 F 77 PTCL-NOS inguinal lymph node 2.3 Animal Studies 2.3.1 Generation of lorlatinib-resistant K299 xenografts For the generation of lorlatinib-resistant K299 xenografts, experiments were carried out as previously described328. Adult female Severe combined immunodeficiency (SCID) (6 weeks old) mice (C.B.17/IcrHanHsd-Prkdc) were kept under standard conditions following the guidelines of the University of Milano-Bicocca ethical committee for animal welfare. The protocol (no. 006/2014) was approved by the Italian Ministry of Health and by the Institutional Committee for Animal Welfare. Lorlatinib was suspended in 0.5% carboxymethylcellulose/0.1% Tween 80. Ten million K299 cells were injected subcutaneously into the left flank of the mice. Once tumours reached an average size of 200 mm3, mice were randomized to receive vehicle alone (n = 3) or lorlatinib (n = 10, starting dose 0.1 mg/kg), orally, twice daily (b.i.d.). Tumour size was evaluated three times per week with calipers, using the formula: tumour volume (mm3) = (d2 × D/2), where D is the longest and d is the shortest diameter. After 21 days, the dose was increased to 0.25 mg/kg b.i.d. and on day 37, to 0.5 mg/kg b.i.d. Each mouse was monitored for tumour growth and the dosage was increased every time the tumour relapsed or on stabilization after partial regression. Treatment was suspended at three different doses: 0.5 mg/kg (n = 4), 1 mg/kg (n = 3) or 2 mg/kg (n = 3). 48 2.3.2 Generation of ALCL PDX NOD scid gamma (NSG) mice were obtained from Charles River and housed in individually ventilated cages (IVCs) under sterile pathogen-free conditions at the University of Cambridge, Central Biomedical Services (CBS) animal facilities, Addenbrooke’s Hospital, Cambridge in groups of 2–5. At the end of procedures, mice were culled by the Schedule 1 method of cervical dislocation in accordance with UK Home Office guidelines. Animal work was carried out under UK Home Office project licence number P4DBEFF63, and personal licence numbers I27A881AC and IF74BBA96 according to the Animals (Scientific Procedures) Act 1986 and were approved by the University of Cambridge Animal Welfare and Ethical Review Body (AWERB). We have complied with all relevant ethical regulations for animal testing and research in the UK. For ALCL PDX establishment, MCs were isolated from a bone marrow (Patient 1) or pleural effusion (Patient 2) sample by gradient centrifugation at 800 x g for 20 minutes at room temperature with brakes off using Lymphoprep according to the standard protocol. Afterwards, MCs were washed once in PBS containing 2% FBS. MCs were suspended in Matrigel diluted 1:2 with PBS and a total volume of 300 µL was injected subcutaneously into the left flank of NSG mice at 6-8 weeks of age using a 30-gauge hypodermic AganiTM needle. Mice were euthanized once tumours reached 15 mm in any direction, tumours were disaggregated and tumour cells vially frozen in 90% FBS, 10% DMSO. For cell line establishment from ALCL PDX tumours, tumours were disaggregated in a petri dish using a 70 M nylon Falcon™ cell strainer and the plunger of a 5 mL Plastipak™ syringe, applying light to medium mechanical force. Afterwards, limiting dilutions were prepared in IMDM supplemented with 20% FBS and 100 U/mL penicillin/streptomycin to establish cell lines. For in vivo ALCL PDX studies, viably frozen PDX cells were thawed, washed in PBS containing 2% FBS and suspended in Matrigel diluted 1:2 with PBS before 0.5 × 106 cells per mouse in a total volume of 300 µL were injected subcutaneously into the left flank of NSG mice at 6-8 weeks of age using a 30- gauge hypodermic AganiTM needle. Mice were monitored for health and tumour size by animal technicians daily. Animals were shaved around the tumour area to assist with precision of measurement and callipers used to measure both the length and width of the tumour. Tumours were measured with manual calipers and tumour volumes estimated using the modified ellipsoid formula: V = ab2/2, where a and b (a > b) are length and width measurements. Once tumours reached 400 mm3, mice were randomly split into three treatment groups and treated daily with the following agents by oral gavage using 22 gauge plastic feeding tubes at 10 µL per gram body weight: vehicle (PBS, 10% DMSO; n = 8), crizotinib (100 mg/kg; n = 8) or brigatinib (25 mg/kg; n = 8). Mice were euthanized once tumours reached 15 mm in any direction. For immunohistochemistry (IHC), tumours were fixed in 10% neutral-buffered formalin for 24 hours, then paraffin-embedded and 3 µm sections cut from central regions. To generate blocks of the established cell lines, 2x107 cells were embedded in 0.5 mL of 1% agar diluted in PBS and processed as above. Tissue sections were stained with hematoxylin and eosin or with antibodies against ALK and CD30. 49 2.4 Immunoperoxidase labelling technique for Anti-ALK Autoantibody Detection 2.4.1 Preparation of COS-1 NPM1-ALK transfectants COS-1 cells were seeded to approx. 1 x 106 cells/mL in 20 mL Dulbecco’s Modified Eagle Medium (DMEM) containing 10% FBS in a T75 cell culture flask at 37 °C in a 5% CO2 cell culture incubator. When the cells reached 50-60% confluence, they were transfected with pcDNA3 NPM1-ALK or pcDNA™ 3.1 vector as negative control using Lipofectamine® 3000 Transfection Reagent following the manufacturers protocol. After 24 hours, the cell culture medium from the transfected COS-1 cells was discarded and the cells washed three times with Dubbecco’s phosphate buffered saline. After washing, the cells were harvested with 10 mL of 1 x trypsin-EDTA for 2 min at 37 °C in a 5% CO2 cell culture incubator. The trypsin was inactivated by adding an equal volume of DMEM containing 10% FBS, the cell suspension subject to centrifugation for 5 minutes at 290 x g and the cell pellet re-suspended in DMEM containing 10% FBS to reach a final concentration of 6-8 x 105 cells/mL. The cell suspension was kept on ice thereafter. Test slides were prepared by varying the volume from 50 – 200 µL cell suspension to each well of the cytocentrifuge. After centrifugation, the Superfrost™ microscope slides were air-died for 5 minutes and subsequently stained in Hematoxylin Solution, Gill No. 3. A volume of cells resulting in an appropriate cell density on the slides were chosen to prepare a large batch of cytocentrifuge slides. The slides were air-dried for 1 hour before fixation in 100% acetone for 10 minutes. After the fixation step the slides were air-dried for another hour until wrapping them back-to-back in pairs in aluminium foil for long-term storage at -20 °C. Several slides were immunostained at this stage to find the efficiency of transfection as described below. 2.4.2 Immunostaining of COS-1 NPM1-ALK transfectants COS-1 NPM1-ALK transfectants were equilibrated at room temperature for 15 minutes before opening the cover foil and the cell spots circled using a Dako wax pen. 100 μL of each serum dilution (1:50, 1:100, 1:250, 1:750, 1:2250, 1:6750, 1:20250, 1:60750 in PBS) were added to a single spot of COS-1 NPM1-ALK transfectants. PBS was used as negative control, and ALK monoclonal antibody (1:1000 dilution) as a positive control. The highest dilution (1:50) was used to check for any background signal on COS-1 pcDNA™ 3.1 vector only transfectants. Slides were incubated for 1 hour at room temperature in a humidified chamber until washing in PBS for 5 minutes. Afterwards, the cell spots were incubated with 100 μL of polyclonal rabbit anti-human IgG/HRP (1:100 dilution in PBS) for 30 minutes. The slides were then washed in PBS for 5 minutes before 3,3’-Diaminobenzidine (DAB) substrate was applied and incubated for eight minutes. After another wash in PBS, slides were counterstained in hematoxylin solution, Gill No. 3, rinsed with tap water, dried and the cover slide mounted with faramount mounting medium. The highest dilution of the serum/plasma sample at which staining of the NPM1-ALK transfectants was still observed was taken as the titre of the antibody. 50 2.5 Protein Microarray Assay for ALK Autoantibody Detection In collaboration with Cambridge Life Sciences (Keith Rawson and Danielle Mack, Ely, UK), a protein microarray assay was developed to determine the presence of ALK autoantibodies in patient serum, plasma, or frozen whole blood. 2.5.1 Antigen spotting process The ALK protein solution was prepared by buffer exchange with a Vivaspin centrifugal filter column with a molecular weight cut-off of 3 kDa in TrisT (50 mM Tris, 0.01% Tween 20, pH 7.4). The concentrated protein solution was diluted to a final concentration of 150 µg/mL with TrisT containing DY-633 carboxylic acid carrier dye (2 µg/mL final) that was filtered with a Minisart NML 0.2 μm hydrophilic filter and transferred into a 96 well V-bottom plate. The dye was used to subsequently monitor the spotting process with Port Array 5000TM. 7.5 pL of triplicate droplets of recombinant ALK (150, 75, 37.5, 18,75 µg/mL) were spotted onto functionalized glass slides with the Nano-PlotterTM NP2.1 controlled by NPC16 NP2/E software. Functionalized glass slides were kind gifts from PolyAN Molecular Surface Engineering, Germany. 2D-Epoxy, 3D-Epoxy, 3D-NHS, 2D-Aldehyde and 3D-Aldehyde functionalized glass slides were tested for their suitability to bind the antigens during the optimization process. 2D- Epoxy glass slides were used in the final assay. Glutathione S-Transferase (150 µg/mL in PBS buffer), and monoclonal anti-human IgG (Fc specific) antibody were spotted as negative and positive controls. Human IgG was spotted to generate a standard curve (200, 100, 50, 25, 12.5 µg/mL). Humidity during the spotting process was kept at 65% with an AIRWIN ultrasonic humidifier controlled by a microprocessor controller CT-1. The protein solutions were cooled to 15 °C by using a programmable temperature controller connected to the microplate holder in the Nano-Plotter. The glass slides were dried for 2 hours before coating of the protein spots with triplicate spots of 7.5 pL microarray stabilizer StabilGuard® Choice. During the spotting process, antigen spotting was monitored with water-sensitive paper glued onto plain glass slides. After spotting, slides were scanned using a Port Array 5000TM and visually quality checked for separation of spots. Two slides from each print run were tested in a microarray assay as described in the section below with anti-GST antibody (1:200 dilution), healthy human plasma controls and positive human patient plasma controls, while remaining slides were stored at 4 °C until usage. 2.5.2 Processing of microarray slides Microarray slides were pre-warmed for 1 hour at room temperature before they were processed using the automated slide processor ZenitUP controlled by the ZenitUP 2.12.51 software package. The slides were blocked with 50 μL of AMIDot™ Activation Diluent and incubated for 30 minutes at room temperature before the plates were washed six times with 50 μL of AUTOZYME™ RF Wash Buffer. Sample (50 μL) diluted in AUTOZYME™ RF Sample Diluent was then transferred to the well and incubated for 30 minutes at room temperature before the plates were washed with 50 μL of AUTOZYME™ RF Wash Buffer. After six washes, a goat anti-human IgG (H&L) – Affinity Pure, DyLight550 Conjugate (1:200 dilution) was used as a reporter. Readings were taken using the Port Array 5000TM. 51 2.6 Cell lines and cell culture The ALCL cell lines DEL355, Karpas 299 (K299)24, SU-DHL-1356, SUP-M2357, Mac-2A and TS (SUP-M2 derived) were cultured in Roswell Park Memorial Institute (RPMI) 1640 medium supplemented with 10% FBS and 1% penicillin and streptomycin (PS). The NB cell lines CHLA-15, CHLA-20, CHLA-42, CHLA-90, CHLA-95, CHLA-171, COG-N-426 (Felix), NB-1643, NB-EBC1, NBL-S and the ALCL cell lines MGS and MTK were cultured in IMDM supplemented with 20% FBS, 1% insulin-transferrin-selenium and 1% PS. CHP-134, GI-ME-N, IMR-32, KELLY, LA-N-1, LA-N-5, LA-N-6, NGP, SK-N-BE(1), SK-N-BE(2), SK-N-FI, SMS-KAN, SMS-KCNR and SMS-LHN cells were cultured in RPMI 1640 supplemented with 10% FBS, 1% ITS and 1% PS. SH-SY5Y and 293FT cells were cultured in DMEM supplemented with 10% FBS and 1% PS. All cells were grown at 37°C in a humidified incubator with 5% CO2. All cells were mycoplasma-free and subjected to periodic in-house testing. 2.7 IC50 determination For IC50 determination, ALCL cell lines were seeded in flat-bottom 96-well plates at approximately 0.5 x 105 cells/mL, treated with decreasing concentrations of ALK TKIs and cultured for 48 hours. Crizotinib, alectinib, lorlatinib and brigatinib were obtained from MedChemExpress and dissolved in 100% dimethyl sulfoxide (DMSO) at a concentration of 1 mM and stored at -20 ⁰C until usage. The end-point viability for each condition was measured with the CellTiter-Blue® Cell Viability Assay in a black 96-well Cellstar plate following the manufacturer’s protocol and analyzed on a SpectraMax i3. 2.8 Cellular proliferation To determine cellular proliferation rates, 0.25 x 105 cells/mL were plated in 6-well plates. Each day, 100 μL of cell suspension was transferred to a well of a 96-well plate, and the cell titre was measured by the CellTiter-Blue Cell Viability Assay. The signal was measured using a SpectraMax i3 plate reader. 2.9 Apoptosis analysis Approximately 500,000 cells were collected, washed with cold PBS, then washed once with and resuspended in 100 μL of the binding buffer provided with the Annexin V Apoptosis Detection Kit APC following the manufacturer’s protocol. Fluorochrome-conjugated Annexin V (5 μL/reaction) was added to the 100 μL cell suspension. Cells were incubated for 30 minutes at room temperature. Afterwards, APC-Annexin V was removed from the cells by centrifugation, washed once in 1 x Annexin V Binding Buffer, then cells were resuspended in 100 μL of 1 x Annexin V Binding Buffer containing 1 mg/mL propidium iodide (PI). The cells were stored in an ice box in the dark until analysis with an Accuri C6 flow cytometer. At least 20,000 events were collected per sample and these data were analyzed with FlowJo software. 2.10 Generation of TKI-resistant ALCL cell lines ALK inhibitor resistant cell lines were established as described previously217. Briefly, ALCL cells were seeded at approximately 0.5 x 106 cells/mL. Crizotinib or alectinib were added at a starting concentration (Table 21), and cells were maintained in fresh drug containing medium changed every 48-72 hours. 52 Cells were passaged once they reached confluence. After every two passages at a given concentration of drug, the concentration of ALK TKI was increased in half-log intervals until a final concentration (Table 21) was achieved. The resulting pool of resistant cells was maintained in TKI containing media thereafter. Table 21 Starting and final TKI concentrations used to generate TKI resistant ALCL cell lines Cell line Crizotinib Alectinib Starting concentration Final concentration Starting concentration Final concentration DEL 25 nM 200 nM 5 nM 100 nM K299 50 nM 300 nM 5 nM 80 nM SU-DHL-1 10 nM 100 nM 5 nM 50 nM SUP-M2 50 nM 600 nM 5 nM 100 nM 2.11 Sequencing of the NPM1-ALK kinase domain region For mutation analyses, genomic DNA was isolated from each of the resistant ALCL cell lines with the QIAamp DNA Mini Kit. The NPM1-ALK kinase domain region was amplified by PCR with the Q5 High- Fidelity PCR Kit from genomic DNA (gDNA) extracts as described in Table 22 using cycle numbers specified in Table 23. Primers were used as described previously358: NPM1-ALK_seq_F 5′-TGCATATTAGTGGACAGCAC-3′ and NPM1-ALK_seq_R 5′-GACTCGAACAGAGATCTCTG-3′. Amplicons were purified with the Zymo DNA Clean and Concentrator-5 kit and then Sanger sequenced by the DNA Sequencing Facility service (University of Cambridge). Table 22 PCR Using Q5 High-Fidelity DNA Polymerase Component Start conc. Amount [µL] Final conc. 5X Q5 Reaction Buffer 10x 5 1x dNTPs 10 mM 0.5 200 µM Forward Primer 10 µM 1.25 0.5 µM Reverse Primer 10 µM 1.25 0.5 µM Template DNA variable variable < 1,000 ng Q5 High-Fidelity DNA Polymerase 2,000 units/ml 0.25 0.02 U/µl Nuclease-free water variable Total 25 Table 23 PCR cycle conditions used to amplify the NPM1-ALK kinase domain region Cycle number Denature Anneal Extend 1 98 °C, 30 sec N/A N/A 2-25 98 °C, 10 sec 65 °C, 30 sec 72 °C, 30 sec 26 N/A N/A 72 °C, 2 min 2.12 RT-qPCR Total RNA was extracted with the RNeasy Plus Mini Kit and quantified using a Nanodrop 1000. RNA (1 µg per cell line) was then reverse transcribed using the High-Capacity RNA-to-cDNA™ Kit. Reverse transcription (RT) reactions were diluted 1/10 and 10 ng (equivalent to the input RNA amount) was used as the template DNA for qPCR using the Power SYBR Green PCR Master Mix with standard reaction conditions on the QuantStudio™ 6 Flex Real-Time PCR System. Double delta Ct (∆∆Ct) analysis was 53 conducted with normalization to GAPDH (∆Ct) and then relative expression level comparison to the negative control expressing scrambled sgRNA (∆∆Ct). All qPCR reactions were performed in technical triplicates. RT-qPCR primers used are listed in Table 24. Table 24 RT-qPCR primers All primers were retrieved from PrimerBank353,359 and synthesized by Sigma-Aldrich. Gene Forward primer Reverse primer ADORA2A CGCTCCGGTACAATGGCTT TTGTTCCAACCTAGCATGGGA ARHGEF9 AATGAGCACTGAGCGTCACTA AGCAGGGTCCTATCTCGCTG BCL10 TCTGGACACCCTTGTTGAATCT TGGAAAAGGTTCACAACTGCTAC BDNF CTACGAGACCAAGTGCAATCC AATCGCCAGCCAATTCTCTTT COPZ2 ATTGTGGATGGCGGTGTGATT TCCTTGGCAGACTGAAGAACC CRK GCGGAGTAGCTGGTACTGG GCGCGAGTTCTCTGAGACG CLYBL GAAGGTCGGGCCTCAAGTAG TTGCCGGGCGTAGAGAATATC EGR4 TCCTCGTCAAGTCCACTGAAG CAGGAGTCGGCTAAGTCCC EML2 GTGGCGGGAACCACTAAGG CCACACCGAGAGCATGTGA EREG GTGATTCCATCATGTATCCCAGG GCCATTCATGTCAGAGCTACACT ETV1 TGGCAGTTTTTGGTAGCTCTTC CGGAGTGAACGGCTAAGTTTATC FAIM2 AGTTCGTCGAGTCTTTGTCAGA TGGGTCCAGAACAGCAAGC FOS GGGGCAAGGTGGAACAGTTAT CGGCTTGGAGTGTATCAGTCA FOXP1 ATGATGCAAGAATCTGGGACTG AGCTGGTTGTTTGTCATTCCTC KRAS GAGTACAGTGCAATGAGGGAC CCTGAGCCTGTTTTGTGTCTAC MET AGCAATGGGGAGTGTAAAGAGG CCCAGTCTTGTACTCAGCAAC MFSD2A GGGAGCAGAGAGAACCCTATG AGGTGTAGGTGCAAAACAAGAC MYC GTCAAGAGGCGAACACACAAC TTGGACGGACAGGATGTATGC NIN CGTGATGGTCACCTGAACCG CGTCCACTCTCATCGAAAGACT NKX2-4 ACCCACGCTACTCGTCAATCT CCTGCCGTTTCATCTTGTACC NPY CGCTGCGACACTACATCAAC CTCTGGGCTGG ATCGTTTTCC NR4A2 ACCACTCTTCGGGAGAATACA GGCATTTGGTACAAGCAAGGT PIK3CD TCAACTCACAGATCAGCCTCC CGCGAAAGTCGTTCACTTCT PIM1 GAGAAGGACCGGATTTCCGAC CAGTCCAGGAGCCTAATGACG PLEKHG6 CCGCCCTACAGAAGCTGAAG GGATAATGGTCGAGAACTCAGGA PRKACA ACCCTGAATGAAAAGCGCATC CGTAGGTGTGAGAACATCTCCC PRRX2 GCACCACGTTCAACAGCAG TCCTTGGCCTTGAGACGGA PSD2 GGATGGCCTGTCAGACTCAGA CAGCCTGCTAAACTCGTTGTT PTGES TCCTAACCCTTTTGTCGCCTG CGCTTCCCAGAGGATCTGC RORC GTGGGGACAAGTCGTCTGG AGTGCTGGCATCGGTTTCG RRAS GACCCCACTATTGAGGACTCC CGGTCGTTAATGGCGAACAC SAGE1 TACCAGGGATCTGCATTCTACC CTGTGGGACCAGTT GACAAGA SAMD4A TCGAGGCTTTGGGCAATCC GAGCTGACGAATCCACTGGT SEMA4A AGCCAGCGAGTTTGACTTCTT CGTGGCGGATGACGTTGAA SLC7A3 GCCATCCATTGTGATCTGCTT GTGGTTCCCAATCAGGTTGTC SPDEF CAGTGCCCGGTCATTGACA CAGCCGGTATTGGTGCTCT SSBP3 GGAACACCCATTATGCCCAGT GACCCATCGGGAAGTTGGAC SURF2 GGGAGCTGCAAGTGATGACAG CGGTACACGGTCGTCTCTCT UBIAD1 AGTGTGCCTCCTACGTGTTG CAGGACACCGTGGGATCTG UTF1 CGCCGCTACAAGTTCCTTAAA GGATCTGCTCGTCGAAGGG YAP1 TAGCCCTGCGTAGCCAGTTA TCATGCTTAGTCCACTGTCTGT GAPDH GGAGCGAGATCCCTCCAAAAT GGCTGTTGTCATACTTCTCATGG NPM1 GGAGGTGGTAGCAAGGTTCC TTCACTGGCGCTTTTTCTTCA MKNK1 AGATGGGCAGTAGCGAACC AGCAATTCAGAGGTCAGCTTG SH2D2A GACTTTCCCTGAGGACCGAAG GCTTGCCCCTGTTTGATGATTG HELZ2 ATCTACATCCGGGAGTATTTCCA TCGTCGGTCAGGCAACAGTA IL10RA CCTCCGTCTGTGTGGTTTGAA CACTGCGGTAAGGTCATAGGA PGBD1 TGCCTGGGATAACCACCCT ATGAGCTGATCCGTGGGGAA GPR161 TGGATCTTTGGTGTAGTGTGGT ATGACCCCGAGGGTTAGCAT P2RY6 GTGTCTACCGCGAGAACTTCA CCAGAGCAAGGTTTAGGGTGTA NPM-ALK CTGTACAGCCAACGGTTTCC GGCCCAGACCCGAATGAGG 54 2.13 Western Blot Protein lysates were prepared by lysing 1 million cells in 40 µL radioimmunoprecipitation assay (RIPA, 25mM Tris HCL pH 7.6, 150mM NaCL, 1% NP-40, 1% sodium deoxycholate, 0.1% SDS) buffer supplemented with Halt Protease and Phosphatase Inhibitor Cocktail for 30 minutes on ice. Cellular debris were removed by centrifugation at 16,000 x g for 20 minutes at 4 °C and the supernatant stored at -20 °C until usage. Proteins were quantified with a PierceTM BCA Protein Assay Kit following the manufacturer’s protocol and analyzed on a SpectraMax i3. Protein lysate (50 µg) were solubilized in Laemmli buffer containing 5% β-mercaptoethanol (250 mM Tris pH 6.8, 10% SDS, 5% beta- mercaptoethanol, 0.02% bromophenol blue, 30% Glycerol) and boiled for 5 minutes at 95 °C. Samples were resolved by SDS-PAGE with a Bio-Rad Mini-Protean TGX 10% gel for 2 hours at 100 V in running buffer (25 mM Tris, 190 mM glycine, 0.1% SDS). Proteins were transferred to a 0.2 μM PVDF membrane using a Trans-Blot Turbo Transfer Pack with a Trans-Blot Turbo Transfer System at 27 V for 7 minutes. Following transfer, membranes were first blocked in blocking buffer (5% Bovine serum albumin (BSA) in TBST (20 mM Tris-HCl pH 8, 150 mM NaCl, 0.1% Tween-20)) for 1 hour at room temperature and subsequently incubated with the the primary antibody diluted in blocking buffer (see Table 25 for antibody dilutions) at 4 °C overnight. Afterwards, the membrane was incubated with the secondary immunoglobulin/HRP diluted in washing buffer (see Table 25 for dilution) for 1 hour at room temperature. Washing was performed with TBST, protein bands were visualized with Immobilon Western Chemiluminescent HRP Substrate and detected by a LAS-4000 Image Analyzer (Fujifilm/Raytek). Table 25 List of antibodies used to detect proteins by Western blot Protein Antibody Antibody name Dilution Company Cat. no. Cas9 1st Anti-Cas9 1:2000 Abcam ab204448 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 ALK 1st Rabbit ALK (D5F3®) XP® antibody 1:1000 Cell Signalling 3633 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 pALK 1st Rabbit Phospho-ALK (Tyr1604) antibody 1:1000 Cell Signalling 3341 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 STAT3 1st Rabbit STAT3 antibody 1:1000 Cell Signalling 4904S 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 STAT1 1st Rabbit STAT1 antibody 1:1000 Cell Signalling 9172 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 pSTAT3 1st Rabbit Phospho-STAT3 (Tyr705) antibody 1:1000 Cell Signalling 9145 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 pSTAT1 1st Rabbit Phospho-STAT1 (Tyr701) antibody 1:1000 Cell Signalling 14994 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 STAT5A 1st Mouse STAT5A (4H1) antibody 1:1000 Cell Signalling 4807 2nd Goat anti-mouse 1:10000 Agilent technologies P0161 pSTAT5A 1st Rabbit Phospho-STAT5 (Tyr694) antibody 1:1000 Cell Signalling 9359 2nd Goat anti-rabbit 1:10000 Agilent technologies P0448 α-Tubulin 1st Mouse anti-α-Tubulin 1:10000 Sigma Aldrich T9026 2nd Goat anti-mouse 1:10000 Agilent technologies P0161 55 2.14 sgRNA cloning To clone individual sgRNAs into lenti sgRNA(MS2)_zeo/lenti sgRNA(MS2)_puro, guide sequences (Table 26) were designed with the Cas9 activator tool360 and synthesized at Sigma Aldrich. They included a 4-bp overhang for the forward (CACC) and complementary reverse (AAAC) oligos to enable cloning into the Bsmb-I site of the lentiviral construct. On-target scoring was performed using the "Rule Set 2" method292. The Microsoft implementation of the scoring model used was Azimuth 2.0. Off-target sites were evaluated using the Cutting Frequency Determination score. Table 26 Oligonucleotides used to generate dsDNA fragments containing the 20 bp target sequence NT is a scrambled 20 bp sequence that is predicted to not target any region of the human genome. Gray nucleotides indicate the overhangs while black nucleotides represent the target sequence. Gene sgRNA Forward ssDNA element Reverse ssDNA element ARHGEF9 1 CACCgCGCAAGCGCCCGCAGTCGCT AAACAGCGACTGCGGGCGCTTGCGc 2 CACCgGGCGGGTATGTCAGTGGCTC AAACGAGCCACTGACATACCCGCCc BCL10 1 CACCgAGGTACTGACAAGCCCAGAC AAACGTCTGGGCTTGTCAGTACCTc 2 CACCgGCAGGGTCTGGGAAAAGGGG AAACCCCCTTTTCCCAGACCCTGCc CRK 1 CACCgACGCGCTCCCTGCCCGAGGG AAACCCCTCGGGCAGGGAGCGCGTc 2 CACCgGGCTGCCGGGAAGGGGCCCG AAACCGGGCCCCTTCCCGGCAGCCc EREG 1 CACCgGGTGCTGCGAACTTTATACT AAACAGTATAAAGTTCGCAGCACCc 2 CACCgGAGCCCCTCCCGGGCCCTAA AAACTTAGGGCCCGGGAGGGGCTCc FOS 1 CACCgAAACGTCACGGGCTCAACCA AAACTGGTTGAGCCCGTGACGTTTc 2 CACCgGGCGCCAGAGGGGTGGCGCG AAACCGCGCCACCCCTCTGGCGCCc NR4A2 1 CACCgCAGCGCGGCGATTGGGCGGC AAACGCCGCCCAATCGCCGCGCTGc 2 CACCgGGCCAGGAGTCCAGGGAGCG AAACCGCTCCCTGGACTCCTGGCCc PLEKHG6 1 CACCgCAGTAATGAGGGCTGAGTAA AAACTTACTCAGCCCTCATTACTGc 2 CACCgGGGCGGGGCGCCGCCGGGGG AAACCCCCCGGCGGCGCCCCGCCCc PRKACA 1 CACCgTGCGGGGGCGTCACAGACAG AAACCTGTCTGTGACGCCCCCGCAc 2 CACCgGGCCTAGGCCAATGAGCGGC AAACGCCGCTCATTGGCCTAGGCCc PSD2 1 CACCgGGGACGGACGGCAGGAGGGA AAACTCCCTCCTGCCGTCCGTCCCc 2 CACCgGGAGCGGAGCCGTGAGCTGG AAACCCAGCTCACGGCTCCGCTCCc PTGES 1 CACCgCTGCAGGGAAAGCACAAAGT AAACACTTTGTGCTTTCCCTGCAGc 2 CACCgGGGCGGTGCTGGCTGCAGGA AAACTCCTGCAGCCAGCACCGCCCc RORC 1 CACCgCCCCCAGTGCTTCTGGACTG AAACCAGTCCAGAAGCACTGGGGGc 2 CACCgGTTTAAGCTCTGCACCACAC AAACGTGTGGTGCAGAGCTTAAACc 3 CACCgGGGAGGGGCAGCCAATCGTA AAACTACGATTGGCTGCCCCTCCCc 4 CACCgCCCCAAGGGGTGCAGTGAGT AAACACTCACTGCACCCCTTGGGGc SAMD4A 1 CACCgCTCCCCCCTTCTTGCAGCTT AAACAAGCTGCAAGAAGGGGGGAGc 2 CACCgGATGGTGATTTCCGGCGTCC AAACGGACGCCGGAAATCACCATCc SPDEF 1 CACCgTATAATGGGAAATCAGGCCC AAACGGGCCTGATTTCCCATTATAc 2 CACCgTTTGTTCAGGTAAATAAGGA AAACTCCTTATTTACCTGAACAAAc UBIAD1 1 CACCgGCCGGCGGCGCGGGCTGGAC AAACGTCCAGCCCGCGCCGCCGGCc 2 CACCgCCGCCCTCCAGCCCACCCTC AAACGAGGGTGGGCTGGAGGGCGGc YAP1 1 CACCgGGCGAGTTTCTGTCTCAGTC AAACGACTGAGACAGAAACTCGCCc 2 CACCgCAAACGCCAAAACTAAAGTT AAACAACTTTAGTTTTGGCGTTTGc ABCB1 1 CACCgGGATAAGTTTGGGTGGAGGA AAACTCCTCCACCCAAACTTATCCc 2 CACCgCACTAATCAGTGAAAACCCA AAACTGGGTTTTCACTGATTAGTGc NMP1 1 CACCgCTGCGCAGACTCTTGGCGGG AAACCCCGCCAAGAGTCTGCGCAGc 2 CACCgTGGAAAGCACGCGTGCGCAC AAACGTGCGCACGCGTGCTTTCCAc 3 CACCgCCGGCGCGCTTGAGCGGGAG AAACCTCCCGCTCAAGCGCGCCGGc MYC 1 CACCgGGTGGGGAGGAGACTCAGCC AAACGGCTGAGTCTCCTCCCCACCc 2 CACCgGAACCCGGGAGGGGCGCTTA AAACTAAGCGCCCCTCCCGGGTTCc MKNK1 1 CACCgGGCGTGACAGGGAAGAGGCG AAACCGCCTCTTCCCTGTCACGCCc 2 CACCgGAGCTGCGCCTGCGCCCTGA AAACTCAGGGCGCAGGCGCAGCTCc IL10RA 1/49 CACCgTAGCGCCCCAGGACAGCCTC AAACGAGGCTGTCCTGGGGCGCTAc 2/50 CACCgGCCCCAGGCGGTAGCCCTGT AAACACAGGGCTACCGCCTGGGGCc 56 Gene sgRNA Forward ssDNA element Reverse ssDNA element 3/72 CACCgGGACAGTGGTTCCCCGTCCG AAACCGGACGGGGAACCACTGTCCc P2RY6 1 CACCgGGCAGCAATGAGCAGAAGCA AAACTGCTTCTGCTCATTGCTGCCc 2 CACCgACTCCAGAGAAGCCAGGAGA AAACTCTCCTGGCTTCTCTGGAGTc SH2D2A 1 CACCgATCTGTGTCACTCTGTGTTT AAACAAACACAGAGTGACACAGATc 2 CACCgTGAAGGTCAGGCAGCAGTAA AAACTTACTGCTGCCTGACCTTCAc HELZ2 1 CACCgCAGCTCGGCCCCCGCTGCGA AAACTCGCAGCGGGGGCCGAGCTGc 2 CACCgCTGCTCGCTGGCGCTTCCCG AAACCGGGAAGCGCCAGCGAGCAGc NT CACCgGGTCCCTCAGGGTGCAACTT AAACAAGTTGCACCCTGAGGGACCc GPR161 1 CACCgGGCCGCAGGGGAGGGGCGCG AAACCGCGCCCCTCCCCTGCGGCCc 2 CACCgTGCGCCTTGCTTTGGAGAGC AAACGCTCTCCAAAGCAAGGCGCAc ADORA2A 1 CACCgTCACTGCAACCTCCACCTCC AAACGGAGGTGGAGGTTGCAGTGAc 2 CACCgCCCAGCTACTCGGGAGGCTG AAACCAGCCTCCCGAGTAGCTGGGc PGBD1 1 CACCgAGTGACCTGGGCGGGGCAGG AAACCCTGCCCCGCCCAGGTCACTc 2 CACCgATGGGCCTGGGGTCCGGCGG AAACCCGCCGGACCCCAGGCCCATc BDNF 1 CACCgGATTCATTTTTTTGTGTTGG AAACCCAACACAAAAAAATGAATCc 2 CACCgTGTGCGGTGGGGAGAGGAGG AAACCCTCCTCTCCCCACCGCACAc CLYBL 1 CACCgGAAGGAGGGCGTGGCTGGCG AAACCGCCAGCCACGCCCTCCTTCc 2 CACCgGTCCTAAGCCCTGGCCGGGA AAACTCCCGGCCAGGGCTTAGGACc COPZ2 1 CACCgACGGGGCCCCGAGGAAAGGG AAACCCCTTTCCTCGGGGCCCCGTc 2 CACCgGATGGGGGAGAACGAGCAAG AAACCTTGCTCGTTCTCCCCCATCc EGR4 1 CACCgCAGGTGGGAAGCGCATCTAC AAACGTAGATGCGCTTCCCACCTGc 2 CACCgGCCTCACCGGGCCGACCGTC AAACGACGGTCGGCCCGGTGAGGCc EML2 1 CACCgCTCCCGATCCCAGGTTCTTT AAACAAAGAACCTGGGATCGGGAGc 2 CACCgGGGGAGAGTGGTTGAGAACA AAACTGTTCTCAACCACTCTCCCCc ETV1 1 CACCgGGGGATTTACGGCTCGTTAT AAACATAACGAGCCGTAAATCCCCc 2 CACCgGGTTACCCTGGATACCCGTC AAACGACGGGTATCCAGGGTAACCc FAIM2 1 CACCgGCGCTGCGGAGCAACCCCAG AAACCTGGGGTTGCTCCGCAGCGCc 2 CACCgTCCCGGGGGAGGGCTAAGGG AAACCCCTTAGCCCTCCCCCGGGAc FOXP1 1 CACCgGCGCCCCGGCCCCCTCCGCG AAACCGCGGAGGGGGCCGGGGCGCc 2 CACCgGTGTGGGGCGCGGCGCGGCG AAACCGCCGCGCCGCGCCCCACACc MFSD2A 1 CACCgCTCCTAGCAATCCGAGAAGC AAACGCTTCTCGGATTGCTAGGAGc 2 CACCgGCTTGGAGAACGTGGCTCGG AAACCCGAGCCACGTTCTCCAAGCc NIN 1 CACCgGGCCCGCGCGGCTCAGGCAG AAACCTGCCTGAGCCGCGCGGGCCc 2 CACCgGGCGGGCGCTCGGAGCGGGA AAACTCCCGCCCGAGCGCCCGCCc NKX2-4 1 CACCgCGTCACAGGCTCAGCTGCCG AAACCGGCAGCTGAGCCTGTGACGc 2 CACCgGTCTGTCGTAAACCTGGCGC AAACGCGCCAGGTTTACGACAGACc NPY 1 CACCgAGGGGCGGGAAGTGGCGGGT AAACACCCGCCACTTCCCGCCCCTc 2 CACCgCGGGAGGGTTGGGGTGTGGG AAACCCCACACCCCAACCCTCCCGc PIK3CD 1 CACCgGATGATGCCCCTCTAGCGGT AAACACCGCTAGAGGGGCATCATCc 2 CACCgGGAAAAACAACAGGTCCTCC AAACGGAGGACCTGTTGTTTTTCCc PIM1 1 CACCgCGGGACTGGGCGACTCCCCT AAACAGGGGAGTCGCCCAGTCCCGc 2 CACCgGGGGAGCAGGGCTGCCGGGC AAACGCCCGGCAGCCCTGCTCCCCc PRRX2 1 CACCgGGATGGAAAACGACAAAACA AAACTGTTTTGTCGTTTTCCATCCc 2 CACCgTGGGTGGGGAGGGTGAAGGG AAACCCCTTCACCCTCCCCACCCAc RRAS 1 CACCgGGACACTTAAGGAGGGGGAC AAACGTCCCCCTCCTTAAGTGTCCc 2 CACCgCGGGAATTCCGAATGAGGCG AAACCGCCTCATTCGGAATTCCCCc SAGE1 1 CACCgCTCAAGGCGGATGGAAGGAA AAACTTCCTTCCATCCGCCTTGAGc 2 CACCgTGGGAGTGATGCTCATGGGG AAACCCCCATGAGCATCACTCCCAc SEMA4A 1 CACCgCAGTATAACCAGCCTAGCAG AAACCTGCTAGGCTGGTTATACTGc 2 CACCgGTGACATGATGGAGAGGCAG AAACCTGCCTCTCCATCATGTCACc SLC7A3 1 CACCgGGCTTTGCAAAAGGATTGCG AAACCGCAATCCTTTTGCAAAGCCc 2 CACCgTGAGGATGGGACGCAGTCTC AAACGAGACTGCGTCCCATCCTCAc SSBP3 1 CACCgGAGCCGCTGCCTGCTCCTGC AAACGCAGGAGCAGGCAGCGGCTCc 2 CACCgTGCCGCGGCCGGCGCTGTCA AAACTGACAGCGCCGGCCGCGGCAc SURF2 1 CACCgGGGCTGGGACGGGTGAGCGC AAACGCGCTCACCCGTCCCAGCCCc 2 CACCgGTTGCAGCTGGGGCTGCGGG AAACCCCGCAGCCCCAGCTGCAACc UTF1 1 CACCgAGGACCCGGCGGGCGGGGCG AAACCGCCCCGCCCGCCGGGTCCTc 2 CACCgAGGGGTCGGTCCTGGCGCTG AAACCAGCGCCAGGACCGACCCCTc IL10 1/86 CACCgAAAGGGGGACAGAGAGGTGA AAACTCACCTCTCTGTCCCCCTTTc 2/87 CACCgTGGCTTTTTAATGAATGAAG AAACCTTCATTCATTAAAAAGCCAc 57 First, dsDNA fragments were generated to be used as the inserts to be ligated into the lenti sgRNA(MS2)_zeo/lenti sgRNA(MS2)_puro backbone vector. Therefore, each oligonucleotide mix (Table 27) was phosphorylated and annealed in a thermocycler using the following conditions: 37 °C for 30 min; 95 °C for 5 min; and ramped down to 25 °C at 5 °C/min. Table 27 Phosphorylation and annealing of single-stranded sgRNA oligonucleotides Component Amount (µL) Final concentration Forward sgRNA 1 10 µM Reverse sgRNA 1 10 µM T4 Ligation buffer, 10x 1 1x T4 PNK 0.5 N/A Nuclease-free water 6.5 N/A Total 10 After the thermocycler run, the annealed dsDNA was diluted 1:10 and the Golden Gate assembly reaction for each sgRNA was set-up as described in Table 28 with cycle conditions specified in Table 29. The ligated products were transformed into NEB High Efficiency Stable Competent E. coli using the manufacturer’s protocol. The constructs were then extracted from bacteria with the QIAprep Spin Plasmid Kit, plasmid DNA was quantified using a Nanodrop 1000 (ThermoFisher) and the sequence verified by standard Sanger sequencing using a U6_promoter_F oligonucleotide (Sigma-Aldrich) 5’- AATGGACTATCATATGCTTACCG-3’ at the DNA Sequencing Facility service (University of Cambridge). Table 28 Golden Gate assembly Component Start conc. Amount [µL] Final conc. T4 Ligase buffer (NEB, cat. no.: B0202S) 10x 2.5 1x DTT (ThermoFisher, cat. no.: 707265ML) 10 mM 2.5 1 mM BSA 2 mg/mL 1.25 0.1 mg/mL Annealed sgRNA 1 µM 1 0.04 µM lenti sgRNA(MS2)_zeo (Addgene, cat. no.: 70183) N/A N/A 1 ng/µL BsmB1 (NEB, cat. no.: R05805) 10,000 U/mL 1 10 U T4 DNA Ligase (NEB, cat. no.: M0202S) 40,000 U/mL 1 N/A Nuclease-free water N/A Total 25 Table 29 PCR cycle conditions used for Golden Gate assembly Cycle number Condition 1-15 37 °C for 5 min, 20 °C for 5 min 2.15 Genome-scale Cas9 transcriptional activation screen 2.15.1 Genome-scale Cas9 transcriptional activation screen design For genome-scale Cas9 transcriptional activation screens, a three plasmid (Figure 8B-D) system – human CRISPR activation library v1– was used271. This library is based on a dCas9-VP64 fusion protein (Figure 8B) that recruits transcriptional complexes to the TSS of target transcripts as well as an altered sgRNA (Figure 8D) to recruit accessory transcriptional co-activators (MS2-p65-HSF1) (Figure 8C) to synergistically interact with the 58 transcriptional complex. Specifically, a hairpin aptamer that selectively binds to the MS2 phage protein was appended to the tetraloop and stem loop no. 2 regions285. Then a separate vector (Figure 8C) was constructed to express MS2 protein fused to the p65 transcription factor and HSF1. This allows for additional recruitment of transcriptional activators to the TSS, leading to >500-fold enhanced overexpression in targeted mRNA levels compared to dCas9-VP64 alone271. Figure 8 The genome-scale Cas9 transcriptional activation screen employed to identify bypass resistance mechanisms to ALK TKIs (A) Programmable transcriptional activation can be achieved using dCas9 and activation domains (e.g., VP64/p65/HSF1) to recruit transcriptional machinery to the transcription start site of the desired gene target, resulting in upregulation of the target transcript. PAM: protospacer adjacent motif, Pol II: RNA polymerase II. (B-D) The vector-system used for the genome-scale Cas9 transcriptional activation screen. The library (lenti sgRNA(MS2)_Zeo) must be combined with additional SAM effectors in a 3-vector format. Blast: Blasticidin, Hygro: Hygromycin, Zeo: Zeocin. (E) Genome-scale Cas9 transcriptional activation screens begin with the construction of a plasmid library encoding the effector protein and sgRNAs. SAM libraries target the 200-bp region upstream of the transcription start site of 23,430 human RefSeq coding isoforms with 3 sgRNAs per isoform. These plasmid libraries are packaged into lentivirus and then transduced into the cell type of interest to generate stably expressing lines for the screen, along with an accessory transcriptional activator complex. A selection pressure –e.g. the ALK TKI crizotinib– is applied and genomic DNA is harvested. Surviving sgRNA sequences (coloured bars) are amplified from genomic DNA and then analyzed by deep sequencing to identify candidate genes. Figure modified from Joung et al., 2017286. A B mRNA upregulation VP64 MS2 sgRNA MS2-P65-HSF1 Pol II Transcriptional activation DNA target Genomic locus dCas9 PAM dCas9 VP64 Blasticidin HygromycinHSF1P65MS2 MS2 sgRNA Zeocin lenti dCas-VP64_Blast lenti MS2-P65-HSF1_Hygro lenti sgRNA(MS2)_Zeo C D MS2-P65-HSF1 lentivirus Naïve ALCL cell line Blasticidin and Hygromycin selection dCas9 and MS2-expressing ALCL cell line Lentiviral sgRNA- expression library Zeocin selection sgRNA- expressing ALCL cell library ALK TKI- resistant ALCL cells ALK TKI selection gDNA isolation sgRNA amplification dCas9-VP64 lentivirus Deep sequencing EGFR KRAS KIT IGF-1R HER Candidate genes E 59 2.15.2 Generation of dCas9 and MS2 expressing cell lines To conduct genome-scale Cas9 transcriptional activation screens, cell lines stably expressing lenti dCAS-VP64_Blast and lenti MS2-P65-HSF1_Hygro were engineered (Figure 8E)271. Briefly, the lentiviral plasmids were individually packaged into lentiviruses and subsequently used for viral transduction in cell lines. First, the expression plasmids were co-transfected into log-phase 293FT cells with second-generation lentiviral packaging plasmids – psPAX2 and pMD2.G at a ratio of 1:1:1 with TransIT-293 in OptiMEM reduced serum medium at 32 °C. After 24 hours the medium was replaced with fresh Dulbecco’s Modified Eagle Medium supplemented with 10% FBS, DNAse I, 20 mM HEPES pH 7.4 and 5 mM MgCl2. Viral supernatant was collected 48 and 72 hr post-transfection, pooled, centrifuged at 360 x g for 5 min, with the resulting supernatant filtered and stored at -80 ⁰C if not used directly. Afterwards, cells were first transduced with viral particles of lenti dCAS-VP64_Blast at a multiplicity of infection (MOI) of < 0.7 then selected in blasticidin S HCl (see Table 30 for concentration) 24 hr post viral transduction for seven days to completion (defined as 0% survival in uninfected cells). Then lenti MS2-P65-HSF1_Hygro particles at an MOI of < 0.7 were applied and stable cells were selected in hygromycin B (see Table 30 for concentration) 24 hr post viral transduction for seven days to completion. Table 30 Antibiotic concentrations that were used for the selection of the transduced cell lines (*) indicates the presence/absence of the pathognomonic translocation t(2;5)(p32;q35) and/or of the fusion gene NPM-ALK. Cell line Diagnosis t(2;5) NPM-ALK* Hygromycin Blasticidin Zeocin Puromycin DEL ALCL cryptic t(2;5) NPM-ALK 200 µg/mL 10 μg/mL 100 μg/mL 500 ng/mL Karpas 299 ALCL (refractory, terminal) t(2;5) NPM-ALK 200 µg/mL 10 μg/mL 100 μg/mL 1 μg/mL SU-DHL-1 ALCL t(2;5) NPM-ALK 125 μg/mL 5 μg/mL 80 μg/mL 1 μg/mL SUP-M2 ALCL (refractory) t(2;5) NPM-ALK 200 µg/mL 10 μg/mL 100 μg/mL 1 μg/mL Mac-2A ALCL, cutaneous (terminal) N/A 125 μg/mL 10 μg/mL 100 μg/mL 1 μg/mL SHSY5Y NB N/A 300 μg/mL 10 μg/mL 750 μg/mL 1 μg/mL CHLA-20 NB N/A 300 μg/mL 10 μg/mL 300 μg/mL 1 μg/mL 2.15.3 Transformation, amplification and preparation of lentiviral sgRNA libraries Aliquots (~1000 ng (50 ng/μl)) of the Human CRISPR Activation Library v1 (SAM - 3 plasmid system) were acquired. Each library (100 ng) was transformed into ElectroMAX Stbl4 Competent Cells via electroporation with the Gene Pulser II Electroporation System according to the protocol supplied with the Stbl4 cells. This process was carried out with 4 replicate reactions. Cells were recovered in SOC Outgrowth Medium and cultured for 1.5 hours at 30 °C before being plated onto ampicillin lysogeny broth (LB) agarose bioassay dishes, which were incubated overnight at 30 °C. The bacterial colonies were then harvested, and the CRISPR plasmid libraries were isolated using the EndoFree Plasmid Maxi Kit. To prepare lentivirus libraries, log-phase HEK293FT cells were seeded at ~50% confluence the day before transfection in reduced (5%) FBS supplemented DMEM to obtain ~80% confluence for transfection. For each T175 flask, 18 μg of sgRNA plasmid library, 16 μg of psPAX2, and 10 μg of 60 pMD2.G were incubated in 4.5 mL of Opti-MEM/TransIT-293 (110 µL of TransIT-293) for 15 minutes at room temperature. 60 hours after transfection, the media was collected and centrifuged at 500 x g at 4 °C for 15 minutes to remove the cellular debris. Aliquots of the supernatant were stored at –80 °C. 2.15.4 Transduction of ALCL cell lines using lentiviral CRISPR libraries To determine optimal virus volumes for achieving a MOI of 0.3, trial infections to determine the effective MOI were set up using ALCL cell lines (specifically DEL, SUP-M2 and K299). Briefly, 1 million cells per well were seeded into a 6-well plate in 10% FBS RPMI 1640. Afterwards, the virus libraries, mixed with fresh media, were added to the cells. Each well received different volumes of viruses (10 μl up to 500 μl) along with a mock (no virus) transduction control. Cells were incubated with the virus for 24 hours and cells from each well were then split into 2 wells in a 6-well plate. One well received 100 μg/mL of zeocin depending on the cell type (Table 30). After 5 days, when no viable cells remained in the no- transduction control duplicate wells incubated with zeocin, viable cells were counted following trypan blue exclusion. The effective MOI was calculated as the average cell count from the triplicate wells incubated with zeocin divided by the average cell count from the triplicates with no selection reagent. The virus volume yielding a MOI of 0.3 was selected to be scaled up for the screens. The virus transduction protocol for the actual screens was scaled up to 20 million cells per T175 flask with a total of 500 million cells being transduced. Stable DEL/SUP-M2/K299 SAM cells were infected with the library at a MOI of 0.3 and a ratio of at least 500 cells/sgRNA were then incubated with zeocin (100 μg/mL) for 7 days. Two separate infections were performed and for each condition 500 cells/sgRNA were collected as input control. The remaining cell pools were subsequently cultured with crizotinib (120/150/300 nM for DEL/SUP-M2/K299) or DMSO for 14 days and harvested for DNA extraction. 2.15.5 Preparation of HiSeq libraries for the screen readout Frozen cell pellets were lysed and their gDNA was extracted with the QIAamp DNA Blood Maxi Kit. PCR of the virally integrated guides was performed on gDNA at the equivalent of 500 cells per guide using Herculase II Fusion DNA Polymerase (Table 31). Oligonucleotides included the Illumina adapters, staggered region and the barcodes (Table 32). The amount of input gDNA for each sample to achieve 500 x coverage of the SAM library was calculated to be approximately 230 μg DNA per replicate, assuming 6.6 μg of gDNA is the yield of 1 million cells. Therefore, 23 parallel 100 μl PCR reactions with 10 μg of input genomic DNA in each reaction were completed in an ABI Veriti Thermal Cycler in a single- step reaction of 24 cycles (Table 33). Table 31 PCR amplification of virally integrated guides Component Amount per reaction (μl) Final concentration UltraPure water 61 5 x Buffer 20 1x 100% DMSO 2 2% 100 mM dNTP 1 1 mM 10 μM F Primer Mix 2.5 0.25 μM 10 μM R Primer 2.5 0.25 μM Herculase II Fusion DNA Polymerase 1 N/A 1 μg/mL gDNA 10 0.1 μg/mL 61 Table 32 Oligonucleotides used for HiSeq library preparation All forward oligonucleotides were mixed at equal molar concentrations, introducing artificial diversity to the library from the varied staggered sequences, then used with one reverse primer (unique 8 bp molecular barcode) per sample. Illumina P5 adaptor, Illumina P7 adaptor, sequencing forward, sequencing reverse, stagger (varied) sequence, index barcode, priming site. A maximum of nine samples were run on one flow cell. Primer Sequence F1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTAAGTAGAGGCTT TATATATCTTGTGGAAAGGACGAAACACC F2 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTATCATGCTTAGCT TTATATATCTTGTGGAAAGGACGAAACACC F3 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTGATGCACATCTGC TTTATATATCTTGTGGAAAGGACGAAACACC F4 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTCGATTGCTCGACG CTTTATATATCTTGTGGAAAGGACGAAACACC F5 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGATAGCAATTC GCTTTATATATCTTGTGGAAAGGACGAAACACC F6 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTATCGATAGTTGCT TGCTTTATATATCTTGTGGAAAGGACGAAACACC F7 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTGATCGATCCAGTT AGGCTTTATATATCTTGTGGAAAGGACGAAACACC F8 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTCGATCGATTTGAG CCTGCTTTATATATCTTGTGGAAAGGACGAAACACC F9 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTACGATCGATACAC GATCGCTTTATATATCTTGTGGAAAGGACGAAACACC F10 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTACGATCGATGGT CCAGAGCTTTATATATCTTGTGGAAAGGACGAAACACC R1 CAAGCAGAAGACGGCATACGAGATGAAGAAGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R2 CAAGCAGAAGACGGCATACGAGATCGTTACCAGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R3 CAAGCAGAAGACGGCATACGAGATGTCTGATGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R4 CAAGCAGAAGACGGCATACGAGATTTACGCACGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R5 CAAGCAGAAGACGGCATACGAGATTTGAATAGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R6 CAAGCAGAAGACGGCATACGAGATTCCTTGGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R7 CAAGCAGAAGACGGCATACGAGATACAGGTATGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT R8 CAAGCAGAAGACGGCATACGAGATAGGTAAGGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCC AAGTTGATAACGGACTAGCCTT R9 CAAGCAGAAGACGGCATACGAGATAACAATGGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGCCA AGTTGATAACGGACTAGCCTT Table 33 PCR cycle conditions used to amplify the sgRNAs’ guide sequence region and to append the Illumina (HiSeq) compatible adapters and barcodes Cycle number Denature Anneal Extend 1 95 °C, 20 sec N/A N/A 2-25 95 °C, 20 sec 60 °C, 20 sec 72 °C, 40 sec 26 N/A N/A 72 °C, 3 min PCR products from all 23 reactions were pooled, purified using the Zymo DNA Clean and Concentrator- 5 kit and a small amount of sample separated on a 2% agarose in 1 x Tris-acetate-EDTA (TAE) gel at 100 V for 2.5 hours to confirm the removal of excess primers. The isolated amplicons were then quantified by qPCR reactions using the KAPA Library Quantification Kit, fragments were analyzed using the 2100 BioAnalyzer System and the 2200 Tape Station, mixed at equal molar concentrations and submitted for HiSeq High Output v4 on 1 x 100 bp mode with 10% PhiX spike at the Bauer Core (Harvard University). 62 2.16 Genome-scale Cas9 mini knockout screen 2.16.1 Genome-scale Cas9 knockout screen design For CRISPR-Cas9 based knockout, the Cas9–sgRNA complex is targeted to a specific sequence in the coding region of a gene and cleaves both strands of the DNA273,274 (Figure 9A). The DNA double-strand break is repaired by NHEJ, an error-prone pathway introducing insertion or deletion mutations that can lead to frameshifts275 and a PTC in the expressed transcript, resulting in NMD of the mRNA and aberrant peptide products that are degraded276. For genome-scale Cas9 knockout screens, a one plasmid system – human lentiCRISPR library v2– was used288 (Figure 9B-C). Figure 9 The genome-scale Cas9 knockout screen employed to identify bypass resistance mechanisms to ALK TKIs (A) Knockout is accomplished by targeted indel formation at a genomic site complementary to the sgRNA. An indel can result in a frameshift, causing early termination, and either production of non-functional protein or non-sense- mediated decay (NMD) of the mRNA transcript. NHEJ, non homologous end joining. PAM: protospacer adjacent motif, (B) Vector-system used for the genome-scale Cas9 knockout screen. (C) Genome-scale Cas9 knockout screens begin with the construction of a plasmid library encoding the effector protein and sgRNAs. These plasmid libraries are packaged into lentivirus particles and then transduced into the cell type of interest to generate stably expressing lines for the screen. A selection pressure –e.g. the ALK TKI crizotinib– is applied and genomic DNA is harvested. Surviving sgRNA sequences (coloured bars) are amplified from genomic DNA and then analyzed by deep sequencing to identify candidate genes. Figure modified from Joung et al., 2017286. BA sgRNA NHEJ Premature stop codon DNA target Genomic locus Cas9 PAM Indel mutation mRNA Deletion of target mRNA via NMD and functional protein Puro lentiCRISPR v2 sgRNA SpCas9 Naïve ALCL cell line Lentiviral sgRNA- expression library Puromycin selection sgRNA- expressing ALCL cell library ALK TKI- resistant ALCL cells ALK TKI selection gDNA isolation sgRNA amplification Deep sequencing EGFR KRAS KIT IGF-1R HER Candidate genes C 63 2.16.2 CRISPR Mini Knockout Screen A CRISPR targeted knockout screen was performed using a mini screen library (Table 34) based on the commercially available GeCKO v2 A or B libraries as described previously288. Table 34 sgRNAs cloned for the CRISPR Mini Knockout Screen Oligonucleotides used to generate dsDNA fragments containing the 20 bp target sequence that were cloned into lentiCRISPR v2. Grey nucleotides indicate the overhangs. NT = non-targeting. UID = unique ID for Addgene # 1000000048. Gene sgRNA UID Forward ssDNA element Reverse ssDNA element ADORA2A 1 HGLibA_01012 CACCgTCTGGCGGAACTCGCGGATA AAACTATCCGCGAGTTCCGCCAGAc 2 HGLibA_01013 CACCgCTCCTCGGTGTACATCACGG AAACCCGTGATGTACACCGAGGAGc 3 HGLibA_01014 CACCgCGTGGCTGCGAATGATCTTG AAACCAAGATCATTCGCAGCCACGc 4 HGLibB_01010 CACCgCTCCACCGTGATGTACACCG AAACCGGTGTACATCACGGTGGAGc 5 HGLibB_01011 CACCgTAGCCATTGGGCCTCCGCTC AAACGAGCGGAGGCCCAATGGCTAc 6 HGLibB_01012 CACCgGAAGGGATTCACAACCGAAT AAACATTCGGTTGTGAATCCCTTCc MYC 1 HGLibA_30663 CACCgAACGTTGAGGGGCATCGTCG AAACCGACGATGCCCCTCAACGTTc 2 HGLibA_30664 CACCgGCCGTATTTCTACTGCGACG AAACCGTCGCAGTAGAAATACGGCc 3 HGLibA_30665 CACCgTGCGTAGTTGTGCTGATGTG AAACCACATCAGCACAACTACGCAc 4 HGLibB_30621 CACCgACAACGTCTTGGAGCGCCAG AAACCTGGCGCTCCAAGACGTTGTc 5 HGLibB_30622 CACCgCGCCGTCGTTGTCTCCCCGA AAACTCGGGGAGACAACGACGGCGc 6 HGLibB_30623 CACCgTCGCTTACCAGAGTCGCTGC AAACGCAGCGACTCTGGTAAGCGAc GPR161 1 HGLibA_19956 CACCgCCCCTCGGCTGGAATCCGTG AAACCACGGATTCCAGCCGAGGGGc 2 HGLibA_19957 CACCgCTATGGCTTCATCTTCCGCG AAACCGCGGAAGATGAAGCCATAGc 3 HGLibA_19958 CACCgCACAGTCGTCATCGTGGAGG AAACCCTCCACGATGACGACTGTGc 4 HGLibB_19928 CACCgCACCTGCCATGAGCGCAGTG AAACCACTGCGCTCATGGCAGGTGc 5 HGLibB_19929 CACCgAGAGACTCCACGTCCCGCTC AAACGAGCGGGACGTGGAGTCTCTc 6 HGLibB_19930 CACCgCCCACACCTCACTGCGCTCA AAACTGAGCGCAGTGAGGTGTGGGc HELZ2 1 HGLibA_21125 CACCgGACGGGCGCACGGCCCCCCT AAACAGGGGGGCCGTGCGCCCGTCc 2 HGLibA_21126 CACCgGCCGTGCGCCCGTCGCCATC AAACGATGGCGACGGGCGCACGGCc 3 HGLibA_21127 CACCgGGAGTGGGTCCGGCGCACGC AAACGCGTGCGCCGGACCCACTCCc 4 HGLibB_21097 CACCgCAACCAGCCCCTGATGTACC AAACGGTACATCAGGGGCTGGTTGc 5 HGLibB_21098 CACCgTCCGCTCACCTCAGAGTGGA AAACTCCACTCTGAGGTGAGCGGAc 6 HGLibB_21099 CACCgGCTCCACAGCCTGCGTGCGC AAACGCGCACGCAGGCTGTGGAGCc IL10 1 HGLibA_22943 CACCgGTTGTTAAAGGAGTCCTTGC AAACGCAAGGACTCCTTTAACAACc 2 HGLibA_22944 CACCgAGCGCCGTAGCCTCAGCCTG AAACCAGGCTGAGGCTACGGCGCTc 3 HGLibA_22945 CACCgTTCACATGCGCCTTGATGTC AAACGACATCAAGGCGCATGTGAAc 4 HGLibB_22912 CACCgCACCTTAAAGTCCTCCAGCA AAACTGCTGGAGGACTTTAAGGTGc 5 HGLibB_22913 CACCgTCGTATCTTCATTGTCATGT AAACACATGACAATGAAGATACGAc 6 HGLibB_22914 CACCgGAAGATGTCAAACTCACTCA AAACTGAGTGAGTTTGACATCTTCc IL10RA 1 HGLibA_22946 CACCgTTCGGCGCCGCACATACAGC AAACGCTGTATGTGCGGCGCCGAAc 2 HGLibA_22947 CACCgGGTCACTGCGGTAAGGTCAT AAACATGACCTTACCGCAGTGACCc 3 HGLibA_22948 CACCgGTATGAGATTGCCATTCGCA AAACTGCGAATGGCAATCTCATACc 4 HGLibB_22915 CACCgTGGGTAGCTGAATCTTCCCG AAACCGGGAAGATTCAGCTACCCAc 5 HGLibB_22916 CACCgCAGGAGCGCCACTTCATAGC AAACGCTATGAAGTGGCGCTCCTGc 6 HGLibB_22917 CACCgTGACGGTCCAGTTGGAGTGC AAACGCACTCCAACTGGACCGTCAc IL10RB 1 HGLibA_22949 CACCgACTTATTGTGTTCAAGTTCG AAACCGAACTTGAACACAATAAGTc 2 HGLibA_22950 CACCgTTAGCCATTATTGGACCCCC AAACGGGGGTCCAATAATGGCTAAc 3 HGLibA_22951 CACCgCTTTCACAGCTCAGTACCTA AAACTAGGTACTGAGCTGTGAAAGc 4 HGLibB_22918 CACCgCTTCCTGATCGGAACAAAGC AAACGCTTTGTTCCGATCAGGAAGc 5 HGLibB_22919 CACCgTGAGAAATCACATTCCGTCA AAACTGACGGAATGTGATTTCTCAc 6 HGLibB_22920 CACCgTTGAGAATGAATACGAAACT AAACAGTTTCGTATTCATTCTCAAc MKNK1 1 HGLibA_29341 CACCgGGAGACGCTGTATCAGTGTC AAACGACACTGATACAGCGTCTCCc 2 HGLibA_29342 CACCgTCGGAGTAGGGTGTTTCGAG AAACCTCGAAACACCCTACTCCGAc 3 HGLibA_29343 CACCgGGCACCTTGAACTTTGGCAT AAACATGCCAAAGTTCAAGGTGCCc 4 HGLibB_29300 CACCgGAACCCCTTCCCATCGCAGA AAACTCTGCGATGGGAAGGGGTTCc 5 HGLibB_29301 CACCgCCTCCTGTCACCATCTGCGA AAACTCGCAGATGGTGACAGGAGGc 6 HGLibB_29302 CACCgGGAGCCTATGCCAAAGTTCA AAACTGAACTTTGGCATAGGCTCCc P2RY6 1 HGLibA_34890 CACCgCGGCAGGCGAAGTCGCCAAA AAACTTTGGCGACTTCGCCTGCCGc 2 HGLibA_34891 CACCgGCCAGCACCGCCGAATACAC AAACGTGTATTCGGCGGTGCTGGCc 3 HGLibA_34892 CACCgAAACGGCCGCGTGCCTTTGT AAACACAAAGGCACGCGGCCGTTTc 4 HGLibB_34844 CACCgCCGGCGTCGAGCGCACTGCC AAACGGCAGTGCGCTCGACGCCGGc 5 HGLibB_34845 CACCgTCACCCAGAAGAAGTTCCGC AAACGCGGAACTTCTTCTGGGTGAc 6 HGLibB_34846 CACCgTCGCCGGCGGAACTTCTTCT AAACAGAAGAAGTTCCGCCGGCGAc PGBD1 1 HGLibA_36215 CACCgCCTGGAGATGAGCTGATCCG AAACCGGATCAGCTCATCTCCAGGc 2 HGLibA_36216 CACCgCACTTCAGGGTGGTTATCCC AAACGGGATAACCACCCTGAAGTGc 64 Gene sgRNA UID Forward ssDNA element Reverse ssDNA element 3 HGLibA_36217 CACCgTGGCCTTACTCATCGGACTG AAACCAGTCCGATGAGTAAGGCCAc 4 HGLibB_36168 CACCgACAGGACATGCACCCAATGG AAACCCATTGGGTGCATGTCCTGTc 5 HGLibB_36169 CACCgCACATCTGAGTCTGACTCGG AAACCCGAGTCAGACTCAGATGTGc 6 HGLibB_36170 CACCgTGCCTGTGTTTAACCCAGTC AAACGACTGGGTTAAACACAGGCAc RORC 1 HGLibA_41780 CACCgGCCTCTTACCCCGTGAGGCT AAACAGCCTCACGGGGTAAGAGGCc 2 HGLibA_41781 CACCgGTGATCCCTTGCAAAATCTG AAACCAGATTTTGCAAGGGATCACc 3 HGLibA_41782 CACCgAAGTCGTCTGGGATCCACTA AAACTAGTGGATCCCAGACGACTTc 4 HGLibB_41728 CACCgAGAGACAGCACCGAGCCTCA AAACTGAGGCTCGGTGCTGTCTCTc 5 HGLibB_41729 CACCgTATCACCTGTGAGGGGTGCA AAACTGCACCCCTCACAGGTGATAc 6 HGLibB_41730 CACCgACTCACCTTGCACCCCTCAC AAACGTGAGGGGTGCAAGGTGAGTc SH2D2A 1 HGLibA_43888 CACCgGGTGCGGTTCAGCGAGAGCG AAACCGCTCTCGCTGAACCGCACCc 2 HGLibA_43889 CACCgTCACCTGTAAGTCAGCACGA AAACTCGTGCTGACTTACAGGTGAc 3 HGLibA_43890 CACCgTGCGGGTCATGTCTGTGATC AAACTCACCTGTAAGTCAGCACGAc 4 HGLibB_43835 CACCgACCTCCGGGTGATGAAGCCA AAACTGGCTTCATCACCCGGAGGTc 5 HGLibB_43836 CACCgCACTCACCGCAGTGTAGCCC AAACGGGCTACACTGCGGTGAGTGc 6 HGLibB_43837 CACCgGGCTTGGTTCCAGAAGACCC AAACGGGTCTTCTGGAACCAAGCCc NT 1 HGLibA_64384 CACCgACGGAGGCTAAGCGTCGCAA AAACTTGCGACGCTTAGCCTCCGTc 2 HGLibA_64386 CACCgATCGTTTCCGCTTAACGGCG AAACCGCCGTTAAGCGGAAACGATc 3 HGLibA_64387 CACCgGTAGGCGCGCCGCTCTCTAC AAACGTAGAGAGCGGCGCGCCTACc 4 HGLibA_64399 CACCgCGACTAACCGGAAACTTTTT AAACAAAAAGTTTCCGGTTAGTCGc 5 HGLibA_64407 CACCgCTATCTCGAGTGGTAATGCG AAACCGCATTACCACTCGAGATAGc 6 HGLibA_64411 CACCgCGCGACGACTCAACCTAGTC AAACGACTAGGTTGAGTCGTCGCGc 7 HGLibA_64420 CACCgCGTGGCCGGAACCGTCATAG AAACCTATGACGGTTCCGGCCACGc 8 HGLibA_64433 CACCgATCGTATCATCAGCTAGCGC AAACGCGCTAGCTGATGATACGATc 9 HGLibA_64440 CACCgCCGCTATTGAAACCGCCCAC AAACGTGGGCGGTTTCAATAGCGGc 10 HGLibA_64443 CACCgTTCGCACGATTGCACCTTGG AAACCCAAGGTGCAATCGTGCGAAc 11 HGLibA_64447 CACCgTACGCTTGCGTTTAGCGTCC AAACGGACGCTAAACGCAAGCGTAc 12 HGLibA_64468 CACCgTCTGGCTTGACACGACCGTT AAACAACGGTCGTGTCAAGCCAGAc 13 HGLibA_64470 CACCgAGCACGTAATGTCCGTGGAT AAACATCCACGGACATTACGTGCTc 14 HGLibA_64472 CACCgACTGCGGAGCGCCCAATATC AAACGATATTGGGCGCTCCGCAGTc 15 HGLibA_64482 CACCgAAGAGTAGTAGACGCCCGGG AAACCCCGGGCGTCTACTACTCTTc 16 HGLibA_64484 CACCgCGGCTCGTTCTACGCACTGA AAACTCAGTGCGTAGAACGAGCCGc 17 HGLibA_64510 CACCgATGCGCTTTAATCGCCGTTC AAACGAACGGCGATTAAAGCGCATc 18 HGLibA_64520 CACCgTGGAAATCGACTGTGCGCTT AAACAAGCGCACAGTCGATTTCCAc 19 HGLibA_64521 CACCgATTAGCCGTTGCCATATCAA AAACTTGATATGGCAACGGCTAATc 20 HGLibA_64534 CACCgTGACGCGATAGAGTTGGCTT AAACAAGCCAACTCTATCGCGTCAc 21 HGLibA_64550 CACCgCGGCTTTGTTGCCCGTAAGC AAACGCTTACGGGCAACAAAGCCGc 22 HGLibA_64557 CACCgCAGTGTCCGAGCGATATTTT AAACAAAATATCGCTCGGACACTGc 23 HGLibA_64563 CACCgACAGCGCTCTCGTGTACTAT AAACATAGTACACGAGAGCGCTGTc 24 HGLibA_64661 CACCgCCGGCAAGAAACTATACTTG AAACCAAGTATAGTTTCTTGCCGGc 25 HGLibA_64663 CACCgCCGCTGTCTCACTAATCTCA AAACTGAGATTAGTGAGACAGCGGc 26 HGLibA_64671 CACCgCAGACGGTTGGTAAGGACGC AAACGCGTCCTTACCAACCGTCTGc 27 HGLibA_64674 CACCgCAGGTTTGCACGCATAGCTA AAACTAGCTATGCGTGCAAACCTGc 28 HGLibA_64689 CACCgCGTTGGGCATAGCGAACACT AAACAGTGTTCGCTATGCCCAACGc 29 HGLibA_64724 CACCgTGGCGGCCCAAACTTAACAC AAACGTGTTAAGTTTGGGCCGCCAc 30 HGLibA_64735 CACCgGCCATTCTAGTCCCGGCATA AAACTATGCCGGGACTAGAATGGCc 31 HGLibA_64744 CACCgATGCTGCAGCTTTACGATCA AAACTGATCGTAAAGCTGCAGCATc 32 HGLibA_64746 CACCgACATACCCCCCTGGTTCAGA AAACTCTGAACCAGGGGGGTATGTc 33 HGLibA_64797 CACCgTTCAATTCACCGAGGGCGCA AAACTGCGCCCTCGGTGAATTGAAc 34 HGLibA_64805 CACCgACCCATTGAGAGTCGCCTGA AAACTCAGGCGACTCTCAATGGGTc 35 HGLibA_64816 CACCgCTGCGTGTCTTGCTCGCATG AAACCATGCGAGCAAGACACGCAGc 36 HGLibA_64818 CACCgTGTCTTCGGATAGGCGGCTT AAACAAGCCGCCTATCCGAAGACAc 37 HGLibA_64833 CACCgCTGGCCGAATCTCACTATGT AAACACATAGTGAGATTCGGCCAGc 38 HGLibA_64876 CACCgTTTTGACTCTAATCACCGGT AAACACCGGTGATTAGAGTCAAAAc 39 HGLibA_64946 CACCgGAACCGACTTGAAGGGGGCT AAACAGCCCCCTTCAAGTCGGTTCc 40 HGLibA_64947 CACCgACTGAGTGGGTAACACGCAT AAACATGCGTGTTACCCACTCAGTc 41 HGLibA_64950 CACCgCCTAAACTCAGACGCACTAC AAACGTAGTGCGTCTGAGTTTAGGc 42 HGLibA_64951 CACCgTACCCTGGATTGTCCTTGCG AAACCGCAAGGACAATCCAGGGTAc 43 HGLibA_64965 CACCgGATCATAATCGCTTCAAGCA AAACTGCTTGAAGCGATTATGATCc 44 HGLibA_65069 CACCgGAACGTAGAAATTCCCATTT AAACAAATGGGAATTTCTACGTTCc 45 HGLibA_65102 CACCgACGCATGCTTCCCAAAGCGT AAACACGCTTTGGGAAGCATGCGTc 46 HGLibA_65134 CACCgGGCGTTAATTAAACTGTTTT AAACAAAACAGTTTAATTAACGCCc 47 HGLibA_65198 CACCgGATTTTAGCTTAGGTCTTAC AAACGTAAGACCTAAGCTAAAATCc 48 HGLibA_65230 CACCgCTCCCAGTACCAGTCAGTTC AAACGAACTGACTGGTACTGGGAGc 49 HGLibA_65280 CACCgCCATTCACAATCCCACTACA AAACTGTAGTGGGATTGTGAATGGc 50 HGLibA_65321 CACCgATCAAAGTGTCTGACTTATT AAACAATAAGTCAGACACTTTGATc 65 The mini screen library consisted of 6 sgRNAs for each gene and 50 non-targeting sgRNAs. sgRNA cloning is described above. The lentiviral library was generated in HEK293T cells by seeding 5x106 cells in a 10 cm dish one day before transfection. For each dish, 15 µg of the targeted screen plasmid library was co-transfected with 5 µg of pMD2.G, 5 µg of pRSV-Rev and 5 µg of pCMVR8.74 in 600 µL of reaction buffer supplemented with 9 µL of Xfect™ Transfection Reagent. Supernatant containing virus was collected 60 hours after transfection and ultra-centrifuged at 24,000 rpm for 2 hours at 4 °C to concentrate the virus. Aliquots were stored at –80 °C. SUP-M2 derived TS and K299 cells were infected with the library at a MOI of 0.3 and a ratio of at least 500 cells/sgRNA and selected with puromycin (0.2- 0.3 μg/mL) for 7 days. Separate infections were conducted for the GeCKO v2 A and B mini libraries. After 7 days, 500 cells/sgRNA were collected as the input control (D0), and genomic DNA was extracted with the Phenol/Chloroform protocol. The remaining cell pools, at a density of 500 cells/sgRNA, were subsequently cultured with crizotinib (80 nM for TS, 100 nM for K299) or DMSO for 14 days and harvested for DNA extraction with the Phenol/Chloroform protocol. Amplification of the specific sgRNAs was performed in a 2-step PCR reaction. First, priming site to lentiCRISPR GeCKO v2 was added using v2Adaptor_F AATGGACTATCATATGCTTACCGTAACTTGAAAGTATTTCG and v2Adaptor_R TCTACTATTCTTTCCCCTGCACTGTTGTGGGCGATGTGCGCTCTG in 13 separate 100 μL reactions with 10 μg genomic DNA per reaction as the input. Next, amplicons were pooled and a second PCR performed in a 100 μL reaction volume with 5 μL of input of the pooled first PCR reaction, each amplified with a reverse primer and forward primer (Table 35). The resulting amplicons were gel extracted. Products were tested for their concentration and specificity using High sensitivity D1000 ScreenTape and qPCR using the KAPA Library Quantification Kit. Libraries were pooled and sequenced using an Illumina NextSeq500 SE on 35bp mode with 150 cycles and with a 20% PhiX spike. Table 35 Oligonucleotides used for HiSeq library preparation Illumina P5 adaptor, Illumina P7 adaptor, sequencing forward, sequencing reverse, stagger (varied) sequence, index barcode, priming site. One forward oligonucleotide was used with one reverse primer per sample. A maximum of ten samples were run on one flow cell. Primer Sequence F1 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTTCTTGTGGAAAGGAC GAAACACCG F2 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTATTCTTGTGGAAAGGA CGAAACACCG F3 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTGATTCTTGTGGAAAGG ACGAAACACCG F4 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTCGATTCTTGTGGAAAG GACGAAACACCG F5 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTTCGATTCTTGTGGAAA GGACGAAACACCG F6 AATGATACGGCGACCACCGAGATCTACACTCTTTCCCTACACGACGCTCTTCCGATCTATCGATTCTTGTGGAA AGGACGAAACACCG R1 CAAGCAGAAGACGGCATACGAGATAAGTAGAGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTTTCTACT ATTCTTTCCCCTGCACTGT R2 CAAGCAGAAGACGGCATACGAGATACACGATCGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTATTCTAC TATTCTTTCCCCTGCACTGT R3 CAAGCAGAAGACGGCATACGAGATCGCGCGGTGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTGATTCT ACTATTCTTTCCCCTGCACTGT R4 CAAGCAGAAGACGGCATACGAGATCATGATCGGTGACTGGAGTTCAGACGTGTGCTCTTCCGATCTCGATTCT ACTATTCTTTCCCCTGCACTGT 66 2.17 Short-hairpin RNA (shRNA) Knockdown pLKO.1-puro shRNA302,303 constructs targeting the gene of interest were constructed using the standard Broad Institute protocol361. First, two oligonucleotides were synthesized to be annealed (Table 36). Using T4 DNA Ligase, the annealed dsDNA was then ligated into the pLKO.1-puro vector, which had been digested with AgeI-HF and EcoRI-HF. The ligated constructs were then transformed in NEB Stable Competent E. coli using the manufacturer’s protocol, the plasmids isolated with the QIAprep Spin Miniprep Kit, plasmid DNA quantified using a Nanodrop 1000 and the sequence verified by standard Sanger sequencing using a LKO.1_F oligonucleotide 5’-GACTATCATATGCTTACCGT-3’. Table 36 Oligonucleotides used to generate dsDNA fragments containing the target shRNA sequence that were cloned into pLKO.1-puro All primers were synthesized by Sigma-Aldrich. Red (Sense), Blue (Loop), Antisense (Green), Black (Term). Gene # Primer sequence (5’ - 3’) STAT3 1 CCGGGCACAATCTACGAAGAATCAACTCGAGTTGATTCTTCGTAGATTGTGCTTTTT 2 CCGGGCTGACCAACAATCCCAAGAACTCGAGTTCTTGGGATTGTTGGTCAGCTTTTT PIM1 1 CCGGACATCCTTATCGACCTCAATCCTCGAGGATTGAGGTCGATAAGGATGTTTTT 2 CCGGCATCCGCGTCTCCGACAACTTCTCGAGAAGTTGTCGGAGACGCGGATGTTTTTTG All pLKO.1-puro constructs were packaged into lentiviral particles by co-transfection of pLKO.1-puro vectors with psPax2 and pMD2.G into 293FT cells as described above. Viruses were harvested 60 hours after transfection and stored in -80 °C. ALCL cells were transduced with viral particles of pLKO.1- puro at an MOI of < 0.7 then incubated with puromycin (see Table 30 for concentration) 24 hours post viral transduction for three days to completion (defined as 0% survival in uninfected cells). 2.18 Drug Synergy Experiments For dose-response curves, cells were treated with log-scale concentrations of ALK inhibitors as previously described, in addition to fixed concentrations of AZD1208 as indicated. Potential synergy between ALK inhibitors and AZD1208 was evaluated by calculating the combination index (CI) based on the Bliss Independence model362. The CI was calculated with the following equation: CI= (Ea+Eb- ((Ea*Eb))/Eab, where Ea indicates the viability effect of drug A (ALK inhibitor) and Eb indicates the viability effect of drug B (AZD1208) and Eab indicates the viability effect of the drug combination. CI < 1 indicates synergism, CI = 1 indicates additivity and CI > 1 indicates antagonism. For dose-response matrices, cells were treated with log-scale concentrations of each compound in 8x8 grids and the DMSO concentration was maintained at 0.3%. Cell viability was measured with CellTiter-Blue as previously described and data were normalized to the average of all untreated wells on the plate (n = 17). All synergy experiments were performed in technical triplicates. 2.19 Immunohistochemistry (IHC) IHC was performed on FFPE sections with the conventional avidin–biotin–peroxidase method. Heat antigen retrieval was performed using citrate buffer pH 6.1 and endogenous peroxidases were quenched by incubating sections in 3% H2O2 in PBS for 10 minutes. First, sections were blocked using the Avidin/Biotin blocking kit. Then, the indicated primary antibodies were added in 1% BSA/PBS and incubated at 4°C overnight. Finally, slides were incubated with biotin-conjugated secondary antibodies 67 using the IDtect Super Stain System – Horseradish peroxidase (HRP) and developed using the AEC substrate kit. Next, the sections were washed with PBS 3 times between each step. The stained slides were mounted with Aquatex and assessed by an experienced pathologist (blindly in respect to clinicopathological parameters and patient outcome) for both the intensity and percentage of positively stained cells: 0 negative, 1+ weak, 2+ moderate and 3+ strong staining. Positive staining was considered if present in >1% of cells. Table 37 Antibodies used to detect proteins by IHC Antibody Dilution Company Cat. no. IL10RA antibody 1:100 Abcam ab94811 IL10RB antibody 1:100 Abcam ab106282 IL10 antibody 1:200 Abcam ab34843 2.20 Chromatin Immunoprecipitation (ChIP) qPCR ChIP-qPCR analysis for IL10, IL10RA, IL10RB and IRF4 was performed on 10 x 106 ALCL cells per sample using an anti-STAT3 (124H6) or anti-GFP antibody. Following treatment with 1000 nM crizotinib or DMSO for 3 hours in growth medium, 10 x 106 cells were fixed with 0.75% formaldehyde for 15 minutes with orbital shaking at room temperature. Subsequently, glycine was added to a final concentration of 125 nM and the reaction was incubated for 5 minutes at room temperature. Next, cells were washed twice with cold PBS, collected by centrifugation then flash frozen in dry ice/isopropanol and stored at -80 °C until use. Cross-linked cell pellets were lysed in 650 μL ChIP lysis buffer (50 mM HEPES-KOH pH7.5, 140 mM NaCL, 1 mM EDTA pH8, 1% Triton X-100, 0.1% sodium deoxycholate, 0.1% SDS) supplemented with cOmplete™ Mini EDTA-free protease inhibitor cocktail per 20 x 106 cells, followed by sonication for a total of 10 minutes with 30 seconds pulses on followed by 30 seconds off, with a Bioruptor® Pico in 15 mL polypropylene centrifuge tubes utilizing sonication beads. Immunoprecipitation reactions were performed by incubating the sonicated samples overnight with 3 μg STAT3 (Cat#: 9139, Cell Signalling) or green fluorescent protein (GFP, Cat#: ab290, Abcam) antibodies at 4 °C. Next, antibodies and chromatin were captured using 50 μL of Protein G Dynabeads per sample for 2 hours at 4 °C. Beads were first washed three times with low salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCL pH 8.0, 150 mM NaCL), followed by three washes with high salt buffer (0.1% SDS, 1% Triton X-100, 2 mM EDTA, 20 mM Tris-HCl pH 8.0, 500 mM NaCL), two washes with LiCL wash buffer (0.25 M LiCL, 1% NP-40, 1% Sodium Deoxycholate, 1 mM EDTA, 10 mM Tris-HCl pH 8.0) and two final washes with TE buffer (10 mM Tris pH 8.0, 1 mM EDTA). DNA was eluted in 200 μL elution buffer (1% SDS, 100mM NaHCO3), the RNA was then digested using 2 μL RNase A (10 mg/mL) at 37 °C for 30 minutes, before cross-links were reversed following incubation at 65 °C for 2 hours with 2 μL proteinase K (20 mg/mL). De-crosslinked DNA was purified with a Zymo DNA Clean and Concentrator-5 kit. ChIP and input DNA were analyzed with SYBR-Green qPCR analysis performed using the QuantStudio™ 6 Flex Real-Time PCR System in accordance with the manufacturer’s protocol using qPCR primers (Table 38) designed based on ChIP-seq dataset GSE117164. 68 Table 38 ChIP-qPCR Sequencing Primers Gene Forward primer sequence Reverse primer sequence IL10 TSS CGGGAAACCTTGATTGTGGC TTCACCTCTCTGTCCCCCTT IL10RA TSS up AGGTGCGAGAGACTGAGGAT GGTTTCCCTTGCTTGCTTGAA IL10RB TSS GGTCGTGTGCTTGGAGGAAG CCTCACCTGACACCAGCAG Control region ATTCCACCTTGTCCAGCCCT GGTTTTATCCCTCTCCCCGAC IRF4 CTCTAAACACCGCGGAGAGG CTTTGCAGAGCGTGTAACGG 2.21 Enforced IL10RA overexpression For enforced IL10RA overexpression, ALCL cells were selected 24 hours after infection with lentivirus particles of pLX302 IL10RA-V5 puro or pLX302 control constructs with 1 μg/mL puromycin before being plated for an experiment. 2.22 mRNA sequencing Libraries were prepared with the TruSeq Stranded mRNA kit protocol according to the supplier’s recommendations. Each transcriptome library was sequenced on an Illumina NextSeq500 as paired- end 75 bp reads. 2.23 Bioinformatics analysis 2.23.1 ChIP-seq Data Analysis ChIP-seq data were converted to the FASTQ format using the fastq-dump tool (v2.8.2; part of SRA toolkit). ChIP-seq reads were aligned to the human genome (hg19) or mouse genome (MM10) with Bowtie2 with settings ‘end-to-end’, ‘very-sensitive’, and ‘no-unal’340. Reads with a mapping quality < 35 (samtools view q = 35) and PCR duplicates (samtools rmdup) were filtered with SAMtools339. Output BAM files were sorted using BEDTools341 “sort” function and converted into BigWig track files using genomeCoverageBed followed by UCSC utility “bedgraphToBigWig”343. The genome browser tracks were visualized in IGV v2.3.92342,343. 2.23.2 Survival Analysis Survival analysis was conducted using the Kaplan-Meier method, with the p-value determined by a log- rank test. PFS was defined as the time to recurrence or relapse, or if a patient had died without recurrence, the time to death. OS was determined as cancer-specific death. Hazard ratio (HR) and multivariate analysis adjusting for clinical parameters including sex, age, disease stage, CNS involvement, MDD/MRD status and ALK-autoantibody status was determined through a Cox proportional hazards model using the ‘‘coxph’’ function in the survival package in R338. In building Kaplan-Meier curves, thresholds for the binary classification of intensity levels were determined using the ‘‘surv_cutpoint’’ function, survival analysis was performed with the ‘‘survfit’’ function, and visualizations were obtained using the ‘‘ggsurvplot’’ function, all in the survminer package in R337. 69 2.23.3 CRISPR Overexpression Screen Deconvolution and Analysis Raw FASTQ sequencing files were demultiplexed with bcl2fastq2 v2.2 allowing for 0 bases mismatch, then matched to the guide sequences from the library files using the Model-based analysis of genome- wide CRISPR/Cas9 knockout (MAGeCK) count function329. Per-sgRNA read counts were then subjected to MAGeCK-MLE analysis, modelling each experimental iteration as a separate batch, as suggested by principal component analysis. MAGeCK-VISPR was used for quality control and visualization, specifically to obtain p-values and to test false discovery rates (FDR). Corresponding gene IDs were mapped to corresponding gene symbols using the biomaRt package in R334. Due to the absence of NT guides in this library, an analysis to control the empirical false positive rate could not be performed. 2.23.4 CRISPR Mini Knockout Screen Deconvolution and Analysis The sgRNA sequences from the mini library files were aligned to the FASTQ file of each sample using BLAST aligner, allowing for a maximum of 2 bases of mismatch. The number of uniquely aligned reads for each sgRNA were counted and the number of reads for each unique sgRNA for a given sample were RPM (Reads Per Million mapped reads) normalized. Boxplots were generated by R boxplot function based on the log2 (RPM normalized sgRNA counts) value, showing the difference of sgRNA representation at different time points, without (vehicle = DMSO) and with crizotinib treatment. The box extends from the first to the third quartile with the whiskers denoting 1.5 times the interquartile range. 2.23.5 mRNA-Seq Data Analysis Data were provided under the MAPPYACTS protocol. Raw paired-end FASTQ files were mapped against the human reference genome (GRCh38.p12) and GENCODE transcriptome annotation version v29351 using Salmon330 (version 0.14.0) with the default parameters. Strand-specific transcript counts were converted into gene counts with the tximport package in R331. Differential gene expression analysis and normalization were performed using the edgeR package in R332. 2.23.6 Gene Set Enrichment Analysis (GSEA) and Gene Ontology (GO) Analysis GSEA was performed with the fgsea package335 in R using pathway annotations from the Kyoto Encyclopedia of Genes and genomes (KEGG)352. GO analysis was performed with the topGO package336 in R. 2.23.7 Gene Expression Analysis Published gene expression data from T-cell lymphoma patients (GSE78513, GSE65823, GSE6338, GSE14879, GSE19069, GSE58445) and lymphoma cell lines (GSE107951, GSE6338, GSE14879, GSE19069, GSE6184, GSE94669) were analyzed to better understand the biological relevance of IL10RA. For GSE78513, GSE65823, GSE6338, GSE14879, GSE19069, GSE58445 and GSE107951, gene expression profiling was performed using the HG-U133-plus2.0 arrays (Affymetrix Inc., Santa Clara, CA). For GSE6184, gene expression profiling was performed using the HG-U133A Array (Affymetrix Inc., Santa Clara, CA). For GSE94669, gene expression profiling was performed using the Illumina HumanHT-12 V4.0 expression beadchip (Illumina Inc., San Diego, CA). 70 The final study consisted of 78 ALK+ ALCL and 48 ALK- ALCL, 159 PTCL-NOS, 110 AITL patients, and 12 reactive Lymphnodes; as well as 64 lymphoma cell lines, 5 ALL cell lines and 1 CLL cell line. Raw CEL files were loaded with the GEOquery package344 and subsequently subject to quality control using the arrayQualityMetrics package346, both in R. Background correction, normalisation and expression calculation were performed using the rma method with the oligo package in R345. Annotation of the transcript clusters was added using the AnnotationDbi package347 in R. The analysis of differential expression using a linear model as well as the correction for multiple testing were conducted using the limma package348 in R. For GSE94669, the raw Illumina text files were loaded then quality assessment and low-level analysis performed with the beadarray package349 in R. Annotations were added using the illuminaHumanv4.db package350 in R. 2.23.8 Co-Expression Analysis Data used for co-expression analyses between IL10RA and IL10 in different cancer types were obtained from the Human Protein Atlas RNA-seq datasets portal. Pearson correlation coefficients to quantify co- expression between genes were assessed using VST-transformed gene expression levels. The threshold for significant Pearson correlation between gene pairs was determined as the Pearson correlation coefficient associated with the 99th percentile among 5,000 random protein- coding/noncoding gene pairs. 2.23.9 Analysis of Public Gene Expression Datasets PIM1 was investigated by Kaplan-Meier EFS analysis with microarray data from primary NB patient cohorts using R2: Genomics Analysis and Visualization Platform (http://r2.amc.nl). The following cohorts were analysed: Kocak (n = 476) (accession: GSE45547)363 and SEQC (n = 498) (accession: GSE49710)364. The cut-off method was selected as ‘scan’ to determine the optimal threshold for each gene, and significance was assessed by the log-rank test. The p-values were corrected for multiple testing using the Bonferroni method. The HR was determined through a Cox proportional hazards model using the ‘‘coxph’’ function in the survival package in R338. 2.23.10 Waterfall Plot The response to crizotinib/brigatinib/vehicle was determined by comparing tumour volume change at time t of study end point or censorship to its baseline: % tumour volume change = ΔVol = ((Vt−Vbaseline)/Vbaseline) × 100%. Tumour volumes were estimated using the modified ellipsoid formula: V = ab2/2, where a and b (a > b) are length and width measurements. The study end point was reached once tumours reached 15 mm diameter in any direction or after 21 days of consecutive treatment. Mice were censored due to tumour ulceration, sudden death, self-mutilation, sickness or if mice remained tumour-free after 21 days of consecutive treatment. 71 CHAPTER 3 Bypass resistance landscape to crizotinib inhibition in ALK+ ALCL 72 3.1 Introduction The first chemotherapy protocol was introduced to paediatric ALCL patients in the 1980s, but EFS and OS rates have scarcely improved and there is a clear need for new, less toxic and more effective therapies in the relapse setting20. The NPM1-ALK fusion protein is the oncogenic driver in 75% of ALK+ ALCL28. A chromosomal translocation gives rise to NPM1-ALK, leading to ectopic expression of this constitutively-active kinase, in turn up-regulating effectors of cell survival and proliferation, including the crucially important JAK/STAT pathway225,365. ALK is an ideal drug target particularly as endogenous expression is limited to neuronal cells during neonatal development104. Therefore, three trials in the USA (COG-ANHL12P1, NCT01979536), France (AcSé CRIZOTINIB, NCT02034981) and Japan (UMIN000028075) have investigated the ALK/MET/ROS1 inhibitor crizotinib in paediatric ALK+ ALCL patients. Though the final results from these trials are yet to come, abrupt relapses following crizotinib discontinuation have been described126,127 suggesting that ALK inhibitors may have to be taken indefinitely and cases of crizotinib resistance have been reported138. Preliminary results from the AcSé CRIZOTINIB trial showed that 5/15 patients progressed and that all cases of progression on crizotinib occurred during the first 3 months following treatment initiation126. An understanding of the molecular pathways enabling tumours to harbor primary drug resistance or to acquire resistance to targeted therapies is critical for precisely predicting patient responses and for the identification of additional targetable pathways to maximize clinical benefit238. The consensus gold standard for identifying ALK inhibitor resistance mechanisms involves WES coupled with RNA-seq of tumour tissues obtained from patients via multiple biopsies throughout their treatment238. Until now fewer than 130 paediatric ALCL patients (NCT01979536, n = 103; NCT02034981, n = 11; UMIN000028075, n = 10) have been treated with crizotinib in a clinical trial setting and the majority of these patients (all those recruited to NCT01979536) have not been re-biopsied at relapse due to ethical constraints and/or the health status of the patient. However, defining a global landscape of resistance mechanisms requires matched presentation-relapse tumour specimens from a sufficiently large number of patients238 230,239. For instance, the cataloguing of epidermal growth factor receptor (EGFR) inhibitor resistance in NSCLC patients with an incidence rate of 18,252 – 54,756 newly diagnosed cases per year in the USA is still incomplete with around 30% of relapsed patients currently presenting with ‘unknown’ resistance mechanisms228–230. This problem is intensified for paediatric malignancies, such as ALK+ ALCL with an incidence rate of approximately 80 newly diagnosed and 16 relapse cases per year in children and adolescents in Europe27. Such an extended discovery phase of resistance mechanisms leads to a deadly lag in the development of salvage therapeutic strategies. To counteract this, we employed genome-wide CRISPR overexpression and knockout screens combined with RNA-seq data from ALK inhibitor relapsed patient tumours to identify biological pathways involved in primary or acquired resistance to ALK-targeted therapy in ALK+ ALCL. The data presented in this chapter of the thesis largely forms sections of a publication in Blood (Prokoph et al.)301, which can be found in Appendix 1. 73 3.1.1 Aims This chapter aims to: • Validate CRISPR/dCas9-VP64 activity in the ALCL cell lines of interest • Apply the validated CRISPR activation platform to screen for potential drivers of resistance to crizotinib inhibition using a genome wide sgRNA library • Obtain a list of candidates that may induce resistance to crizotinib • Perform individual validation assays for each of the candidate genes to confirm their capability to induce resistance to crizotinib • Determine the overexpression levels of candidates in TKI resistant cell lines and an orthotopic xenograft model • Determine which targets identified by the CRISPR screens are of potential clinical relevance by RNA-seq of ALK inhibitor relapse compared to sensitive patient tumours 3.2 Validation of the dCas9-VP64 induced overexpression phenotype In order to comprehensively define potential mechanisms driving resistance to crizotinib in a high- throughput manner, we established a CRISPR-based overexpression system in ALCL cell lines271,286. Transcriptional upregulation is achieved by directly fusing VP64 to catalytically inactive Cas9 (dCas9) and further recruiting the transcriptional activation domains p65 and HSF1, eventually recruiting the transcriptional machinery to the transcription start site (TSS) of the desired target genes. Using this system, we first upregulated expression of the ATP binding cassette subfamily B member 1 (ABCB1) (Figure 10A), a transporter expressed in the liver and blood-brain barrier to efflux toxic agents366, that was previously shown to mediate crizotinib resistance in ALK+ NSCLC367. In doing so, we were able to increase the IC50 of crizotinib for 3 of 4 ALK+ ALCL cell lines (SU-DHL-1/K299/DEL) but not for an ALK- ALCL cell line (Mac-2A) (Figure 10B), confirming that sensitivity to crizotinib can be readily manipulated. (legend on next page) -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 1-2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) ABCB1 sgRNA 1 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 1 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 1 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 1 N o rm a liz e d V ia b ili ty ( % ) Log10 (Crizotinib), [nM] Mac-2A, ALK-K299, ALK+ A B S U -D H L- 1 K 29 9 S U P -M 2 D E L M ac -2 A 0 50 100 150 200 500 1000 1500 2000 F o ld C h a n g e E x p re s s io n (r e la ti v e t o N T s g R N A ) ABCB1 sgRNA 1 ABCB1 sgRNA 2 SU-DHL-1, ALK+ SUP-M2, ALK+ DEL, ALK+ -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 ** *** -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 *** *** *** *** *** ** *** -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 IC50 (nM) SU- DHL- 1 K299 DEL NT sgRNA 1.5 16.2 22.3 ABCB1 sgRNA 1 4.4 23.1 36.3 ABCB1 sgRNA 2 19.0 57.4 137.5 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) 1 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) 1 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) 1 -2 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) 1 -2 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) 1 74 Figure 10 ABCB1 overexpression induces resistance to crizotinib (A) Fold change in expression levels of ABCB1 modulated by CRISPR overexpression for two sgRNAs in the indicated ALCL cell lines. Data are represented as means ± SD of technical replicates, n = 3. (B) Overexpression of ABCB1 modified sensitivity to crizotinib in SUDHL-1, K299 and DEL, but not SUP-M2 or Mac-2A cell lines. Viability of the indicated ALCL cell lines based on normalized CellTiter-Blue fluorescence reads on exposure to increasing concentrations of crizotinib for 48 hours when expressing 1 of 2 of the indicated sgRNAs inducing overexpression of ABCB1. Data are represented as means ± SD, n = 3. Two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. Reproduced from Prokoph et al.301. Next, to test the efficiency of the CRISPR overexpression system in ALCL cell lines, we used a panel of validated sgRNAs300 targeting the promoters of 15 genes, which were previously shown to lead to crizotinib resistance in EML4-ALK+ NSCLC232. We found that the ability of most sgRNAs to achieve significant overexpression was highly cell line dependent. Specifically, we observed the highest activation of expression in SUP-M2 and DEL and the lowest in K299 cell lines (Figure 11). Figure 11 The dCas9-VP64-based CRISPR activation system induces overexpression of various genes in different ALCL cell lines Fold change in expression levels modulated by CRISPR overexpression for two sgRNAs per gene versus non- targeting (NT) control sgRNA in ALCL cell lines. Data are represented as means of technical replicates, n = 3. An absolute log fold change of 2.5 is indicated by dashed lines. Reproduced from Prokoph et al.301. 3.3 CRISPR Overexpression Screens Identify Genes Modulating Crizotinib Sensitivity in ALCL Cell Lines To account for this inter-cell line variability, we applied our CRISPR-based overexpression platform to screen for potential drivers of resistance to crizotinib in three different ALCL cell lines using a genome wide sgRNA library containing 70,290 sgRNAs targeting 23,430 protein-coding genes271 (Figure 12A). dCas9-VP64/MS2-P65-HSF1-expressing K299, DEL and SUP-M2 cells were transduced with the library and selected in zeocin for 7 days (day 0). For library screening, we exposed the selected cells to crizotinib or DMSO for 14 days (day 14). Genomic DNA was isolated from the cells on days 0 and 14, and deep-sequenced to measure read counts for each sgRNA. Following treatment, changes in abundance of each sgRNA were assessed using the MAGeCK count function329 and analyzed for quality control (Figure 12B-D). We identified a host of genes enriched in cells exposed to crizotinib compared -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 N o rm a liz e d V ia b ili ty ( % ) Log10 (Crizotinib), [nM] K299, ALK+SU-DHL-1, ALK+ SUP-M2, ALK+ DEL, ALK+ Mac-2A, ALK- Mac-2A, ALK-K299, ALK+ A B Fold Change Expression (relative to NT sgRNA) Fold Change Expression (relative to NT sgRNA) Fold Change Expression (relative to NT sgRNA) Fold Change Expression (relative to NT sgRNA) Fold Change Expression (relative to NT sgRNA) S U -D H L- 1 K 29 9 S U P -M 2 D E L M ac -2 A 0 50 100 150 200 500 1000 1500 2000 F o ld C h a n g e E x p re s s io n (r e la ti v e t o N T s g R N A ) ABCB1 sgRNA 1 ABCB1 sgRNA 2 SU-DHL-1, ALK+ -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) ABCB1 sgRNA 2 SUP-M2, ALK+ DEL, ALK+ -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 *** *** *** *** *** ** *** -2 0 2 0 50 100 Log10 [Crizotinib], (nM) N o rm a liz e d v ia b ili ty ( % ) NT sgRNA ABCB1 sgRNA 2 75 to D0, including genes with known relevance to ALCL disease biology, such as STAT3225,365, RORC321, MYC368 and IRF4321,368,369 (Figure 12E). Figure 12 CRISPR Overexpression Screens Identify Genes Modulating Crizotinib Sensitivity in ALCL Cell Lines (A) Schematic of the CRISPR-dCas9-based overexpression screen for the identification of genes whose activation modifies sensitivity to crizotinib in ALCL cell lines: Transcriptional activation is achieved by fusing/recruiting catalytically inactive Cas9 (dCas9) to transcriptional activation domains (VP64/p65, HSF1) to recruit the transcriptional machinery to the transcription start site of the desired target genes. As a first step a SAM (synergistic activation mediator) complex composed of catalytically inactive Cas9 (dCas9) fused to the transcriptional activator VP64 and further activation domains (p65, HSF1) are transduced into ALCL cell lines to generate stably expressing lines for the screen. Afterwards ALCL cell lines are transduced with the sgRNA library and selected with zeocin for 7 days (day 0). Next, crizotinib/DMSO selection pressure is applied, and genomic DNA is harvested on day 0 and after 14 days of treatment. The sgRNA regions are amplified from genomic DNA and then analyzed by next- generation sequencing followed by statistical analyses to identify candidate genes. (B) Correlation of sgRNA read counts across seperate infection replicates for the indicated ALCL cell lines. ρ, Spearmann correlation coefficient. (C) Principle Component (PC) analysis of sgRNA read counts across the sequencing libraries for the indicated ALCL cell lines. D = day, rep = separate infection replicate. (D) Distributions of sgRNA read counts across the CRISPR overexpression sequencing library for the indicated ALCL cell lines. D = day, rep = separate infection replicate. (E) Global changes in sgRNA representation of genes before and after 14 days of treatment with (120/150/300 nM for DEL/SUP-M2/K299) crizotinib, detected in at least two of the three ALCL cell lines tested. Reproduced from Prokoph et al.301. STAT3, a well-known downstream mediator of NPM1-ALK225,365, was the most significantly enriched gene in all three CRISPR overexpression screens (Figure 12E, Figure 13A), thereby confirming the validity of this approach. In addition, NPM1 was also significantly enriched in the screens (Figure 12E, Figure 13B). Transcription of NPM1-ALK is driven by the NPM1 promoter3. It is therefore likely that 1 2 3 4 5 6 7 8 1 2 3 4 5 6 Log2 normalized sgRNA enrichment -L o g 1 0 p -v a lu e RORC NPM1 STAT3 SH2D2A MKNK1 HELZ2 IL10RA P2RY6 ADORA2A MYCIRF4 GPR161 PGBD1 EA MS2-P65-HSF1 dCas9-VP64 library of 70,290 sgRNAs naïve ALCL cell line crizotinib resistant ALCL cells ALCL SAM cell line day 0 day 14 DMSO crizotinib gDNA isolation sgRNA amplification deep sequencing identification of surviving sgRNA sequences li d sgRNA enrichment -L o g 1 0 p v a lu e D14 DMSO rep2 0. 1 1 10 10 0 10 00 10 00 0 10 00 00 0.1 1 10 100 1000 10000 100000 0. 1 1 10 10 0 10 00 10 00 0 10 00 00 0.1 1 10 100 1000 10000 100000 sgRNA read count for rep1 s g R N A r e a d c o u n t fo r re p 2 0. 1 1 10 10 0 10 00 10 00 0 10 00 00 10 00 00 0 0.1 1 10 100 1000 10000 100000 1000000 B C DELSUP-M2K299 sgRNA read counts for rep1 s g R N A r e a d c o u n ts f o r re p 2 ρ=0.9462 ρ=0.9624 ρ=0.9023 PC1 P C 2 DELSUP-M2K299 D SUP-M2K299 DEL s g R N A r e a d c o u n ts (x 1 0 e 3 ) s g R N A r e a d c o u n ts (x 1 0 e 3 ) s g R N A r e a d c o u n ts (x 1 0 e 3 ) D14 DMSO rep1 D14 Crizotinib rep1 D0 rep 2D0 rep1 D14 Crizotinib rep 2 76 overexpression of NPM1 could mediate sensitivity to ALK inhibition by driving overexpression of NPM1- ALK. Indeed, sgRNA-mediated overexpression of NPM1 in 3 ALK+ ALCL cell lines confirmed co- overexpression of NPM1 and NPM1-ALK in these cell lines (Figure 13C). These data are consistent with previous studies that show overexpression of EML4-ALK or NPM1-ALK as a resistance mechanism to ALK TKIs in ALK+ NSCLC and ALCL respectively210,226,327,370,371. Figure 13 CRISPR Overexpression Screens Identified STAT3 and NPM1 to Modulate Crizotinib Sensitivity in ALCL Cell Lines (A) Read counts of sgRNAs targeting STAT3 before and after a 14-day incubation with crizotinib (upper panel, 120/150/300 nM for DEL/SUP-M2/K299) or DMSO (lower panel) in the indicated ALCL cell lines. Data are presented as means, n = 2. The p values were calculated using a Wilcoxon matched pairs signed rank test. D = day. (B) Read counts of sgRNAs inducing overexpression of NPM1 before and after a 14-day incubation with crizotinib (upper panel, 120/150/300 nM for DEL/SUP-M2/K299) or DMSO (lower panel) in the indicated ALCL cell lines. Data are presented as means, n = 2. The p values were calculated using a Wilcoxon matched pairs signed rank test. D = day. (C) Fold change in expression levels of NPM1 (bottom panel) and NPM1-ALK (top panel) expression level for each of the 3 sgRNAs inducing overexpression of NPM1 versus non-targeting (NT) control sgRNA in ALCL cells. Data are represented as means ± SD, n = 3. Reproduced from Prokoph et al.301. 3.4 Validation of candidate genes identified in the screen 3.4.1 Overexpression-based validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Cell Lines We selected the 10 most significantly enriched genes that were shared between at least two ALK+ ALCL cell lines for further validation (Figure 12E,Table 39). First, we overexpressed these genes as well as NPM1 (positive control), using 2 sgRNAs per gene, in the 3 ALCL cell lines previously employed for the screens as well as additional ALK+ (SU-DHL-1) and ALK- (Mac-2A) cell lines. After confirming overexpression levels, growth inhibition in the presence of crizotinib was assessed (Figure 14). N P M 1 sg R N A 1 N P M 1 sg R N A 2 N P M 1 sg R N A 3 0 2 4 6 8 10 NPM1 sgRNA 1 NPM1 sgRNA 2 Fold Change Expression (relative to NT sgRNA) NPM1 sgRNA 3 N P M 1 sg R N A 1 N P M 1 sg R N A 2 N P M 1 sg R N A 3 0 1 2 3 4 5 Fold Change Expression (relative to NT sgRNA) NPM1 sgRNA 1 NPM1 sgRNA 2 NPM1 sgRNA 3 N P M 1 sg R N A 1 N P M 1 sg R N A 2 N P M 1 sg R N A 3 0 1 2 3 4 5 NPM1 sgRNA 1 NPM1 sgRNA 2 Fold Change Expression (relative to NT sgRNA) NPM1 sgRNA 3 N P M 1 sg R N A 4 4 N P M 1 sg R N A 4 5 N P M 1 sg R N A 4 6 0 1 2 3 4 5 NPM1 sgRNA 44 NPM1 sgRNA 45 NPM1 sgRNA 46 N P M 1 sg R N A 4 4 N P M 1 sg R N A 4 5 N P M 1 sg R N A 4 6 0 1 2 3 4 5 NPM1 sgRNA 44 NPM1 sgRNA 45 NPM1 sgRNA 46 N P M 1 sg R N A 4 4 N P M 1 sg R N A 4 5 N P M 1 sg R N A 4 6 0 2 4 6 8 10 NPM1 sgRNA 44 NPM1 sgRNA 45 NPM1 sgRNA 46C SUP-M2SU-DHL-1 F o ld C h a n g e E x p re s s io n (n o rm a liz e d t o N T s g R N A ) DEL F o ld C h a n g e E x p re s s io n (n o rm a liz e d t o N T s g R N A ) F o ld C h a n g e E x p re s s io n (n o rm a liz e d t o N T s g R N A ) N P M 1 -A L K N P M 1 N P M 1 -A L K N P M 1 N P M 1 -A L K N P M 1 A B s g R N A c o u n ts DELSUP-M2K299 s g R N A c o u n ts DELSUP-M2K299 p < 0.05 p < 0.05 p < 0.05 p < 0.05 p < 0.05 p < 0.05 s g R N A c o u n ts DMSO DMSO DMSO DMSO DMSO DMSO Crizotinib Crizotinib Crizotinib Crizotinib Crizotinib Crizotinib ns ns ns ns ns ns s g R N A c o u n ts 77 Table 39 Candidate genes and their relevance in ALCL and other cancers To the best of our knowledge this information is currently publicly not available (N/A). Gene Protein Relevance in ALCL disease biology Role in cancer progression/ treatment resistance IL10RA Interleukin-10 receptor subunit alpha Merkel and colleagues372 showed that knockdown of IL10RA with shRNA in K299 cells led to increased cell death. Béguelin et al.373 identified that IL10RA is amplified in 21% of DLBCLs and its expression is a predictor for patient survival. GPR161 G-protein coupled receptor 161 N/A Feigin et al.374 identified that GPR161 overexpression in Triple-negative breast cancer (TNBC) correlates with poor prognosis by promoting cell proliferation. ADORA2A Adenosine receptor A2a N/A Merighi et al.375 summarized the role of ADORA2A in cancer progression and therapy resistance, as well as presented clinical candidates that are tested as single agents or in combination with immunotherapeutic agents. P2RY6 P2Y purinoceptor 6 N/A Wilson et al.232 identified P2RY6 as a putative resistance driver in crizotinib resistant ALK+ NSCLC MKNK1 MAP kinase-interacting serine/threonine-protein kinase 1 N/A Xie et al.376, Hou et al.377 and Dreas et al.378 summarized the role of MKNK1 in cancer including mantle cell lymphoma, chronic lymphocytic leukemia (CLL) and DLBCL379. RORC Nuclear receptor ROR- gamma Mathas and colleagues321 have shown that pharmacological inhibition of RORC as single treatment leads to a reduction in cell viability that is significantly enhanced when RORC inhibitors (SR2211, SR1903 or GSK805) are used in combination with crizotinib. Ng et al.312 performed CRISPR knockout screens in ALCL cell lines and confirmed RORC as vulnerability in ALK+ and ALK- ALCL. Wilson et al.232 identified RORC as a putative resistance driver in crizotinib resistant ALK+ NSCLC PGBD1 PiggyBac transposable element-derived protein 1 N/A N/A SH2D2A SH2 domain-containing protein 2A N/A N/A HELZ2 Helicase with zinc finger domain 2 N/A N/A MYC Myc proto-oncogene protein Lenz and colleagues368 have shown that MYC is essential for ALCL survival, as both knockdown of MYC and pharmacologic inhibition of MYC signaling were toxic to ALCL cell lines. Ng et al.312 performed CRISPR knockout screens in ALCL cell lines and confirmed MYC as vulnerability in ALK+ and ALK- ALCL. Lyapichev at al.380 identified that MYC expression was associated with a shorter overall survival in ALK+ ALCL. 78 Figure 14 Overexpression of candidate genes identified from the SAM screen induce resistance to crizotinib Dragonfly plots of crizotinib efficacy measurements in the indicated ALCL cell lines expressing sgRNAs targeting the indicated genes based on normalized CellTiter-Blue fluorescence reads following 48 hours of treatment with crizotinib. Six-point dose response curve experiments were performed as described in Figure 10B. An sgRNA was defined as modifying the sensitivity to crizotinib treatment when the difference between the sgRNA to non-targeting (NT) control sgRNA in a two-sample t test was p =/< 0.05. Data are represented as means, n = 3. Fold change of expression levels (blue-gray) modulated by CRISPR overexpression for two sgRNAs relative to non-targeting (NT) control sgRNA was determined at baseline. Data are represented as means ± SD of technical replicates, n = 3. Ctrl = control. Reproduced from Prokoph et al.301. The most consistent targets, modifying crizotinib sensitivity in all ALK positive cell lines were IL10RA and ADORA2A (Figure 15). Figure 15 Overexpression-based validation of candidate genes Fold change of expression levels of the CRISPR screen candidate genes modulated by CRISPR overexpression for two sgRNAs relative to non-targeting (NT) control sgRNA in 4 ALK+ ALCL cell lines (K299/DEL/SUP-M2/SU- DHL-1) plotted against the total number of gene specific sgRNAs that modified sensitivity to crizotinib in 48-hour CellTiter-Blue assays. Reproduced from Prokoph et al.301. 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold difference 0 1 0 1 0 0 5 0 0 0 s g 1 Normalized fold difference 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold difference C tr l IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1-ALK K299, ALK+ 0 10 100 5000101005000 0 sg2 fold change expression (relative to NT sgRNA) sg1 fold change expression (relative to NT sgRNA) 0 1 0 1 0 0 5 0 0 0 s g 2 N o rm a liz e d fo ld d iffe re n c e 0 1 0 1 0 0 5 0 0 0 s g 2 Noralized fold difference A 0 10 100 5000 SUP-M2, ALK+ 101005000 0 sg2 fold change expression (relative to NT sgRNA) sg1 fold change expression (relative to NT sgRNA) 0 1 0 1 0 0 5 0 0 0 s g 2 N o rm a liz e d fo ld d iffe re n c e 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold difference DEL, ALK+ 0 10 100 5000101005000 0 sg2 fold change expression (relative to NT sgRNA) sg1 fold change expression (relative to NT sgRNA) 0 1 0 1 0 0 5 0 0 0 s g 2 N o rm a liz e d fo ld d iffe re n c e 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold difference IL10RAIL10RA C tr l GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1-ALK C tr l GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1-ALK 0 1 0 1 0 0 5 0 0 0 s g 1 Normalized fold difference 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold differenc 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold differenc IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1 s g 1 0 1 2 IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1 s g 1 0 1 2 IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1 s g 1 0 1 2 0 1 0 1 0 0 5 0 0 0 s g 1 Normalized fold difference 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold difference 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold differenc 0 1 0 1 0 0 5 0 0 0 s g 1 Normalized fold difference The colors of the body segments indicate how many sgRNAs modified sensitivity to each gene. The width of the wings indicates the gene expression fold change achieved with sgRNA1 (left wings) and sgRNA2 (right wings). 2 sgRNAs modified sensitivity to crizotinib 1 sgRNA modified sensitivity to crizotinib Insufficient increase in gene expression 0 sgRNAs modified sensitivity to crizotinib Gene expression fold change by sgRNA1 Gene expression fold change by sgRNA2 SU-DHL-1, ALK+ Mac-2A, ALK- 0 10 100 5000101005000 0 sg2 fold change expression (relative to NT sgRNA) sg1 fold change expression (relative to NT sgRNA) 0 1 0 1 0 0 5 0 0 0 s g 2 N o rm a liz e d fo ld d iffe re n c e 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold difference 0 10 100 5000101005000 0 sg2 fold change expression (relative to NT sgRNA) sg1 fold change expression (relative to NT sgRNA) 0 1 0 1 0 0 5 0 0 0 s g 2 N o rm a liz e d fo ld d iffe re n c e 0 1 0 1 0 0 5 0 0 0 s g 2 Normalized fold differnce IL10RAIL10RA C tr l GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1-ALK C tr l GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1-ALK IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1 s g 1 012 IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGBD1 SH2D2A HELZ2 MYC NPM1 s g 1 012 0 2 4 6 8 10 12 14 0 1 2 3 4 5 log2 normalized median fold change expression (relative to NT sgRNA) to ta l n u m b e r o f s g R N A s m o d u la ti n g s e n s it iv it y t o c ri z o ti n ib GPR161 IL10RA RORC ADORA2A PGBD1 P2RY6 MKNK1 HELZ2 MYC SH2D2A NPM1 T o ta l n u m b e r o f g e n e s p e c if ic s g R N A s m o d u la ti n g s e n s it iv it y t o c ri z o ti n ib Log2 normalized median fold chang expression (relative to NT sgRNA) 79 3.4.2 Knockout-based validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Cell Lines We next reasoned that if overexpression of a gene would decrease sensitivity to crizotinib, then knockout of the same gene should increase sensitivity to crizotinib. To address this, we first analyzed a publicly available CRISPR knockout screen dataset312 by Ng et al. of 5 ALK+ and 1 ALK- ALCL cell line. While MYC and RORC were identified as vulnerabilities in ALK+ ALCL, knockout of the other 8 gene hits did not induce cell death. Figure 16 CRISPR knockout screen dataset by Ng et al. identifies MYC and RORC as vulnerabilities in ALK+ and ALK- ALCL (A) Genes ranked by Z-score. (B) Corresponding false-discovery rate (FDR) q-values. (C) Corresponding gene expression level; RPKM, reads per kilobase of transcript, per million mapped reads. (D) Combined gene expression levels and FDRs identify vulnerabilities as true or false. Having established that ADORA2A, GPR161, HELZ2, IL10RA, MKNK1, P2RY6, PGBD1 and SH2D2A are non-essential genes in ALK+ ALCL, we next performed a mini CRISPR knockout screen in two ALCL cell lines targeting the same 10 candidate genes. K299/SUP-M2 cells were transduced with a GeCKO v2 mini library containing 6 sgRNAs per gene plus 50 NT sgRNAs and selected with puromycin for 7 days (day 0) before exposure to crizotinib or DMSO for 14 days (day 14) and processing as previously conducted for the activation screen (Figure 17A). Knockout of IL10RA, P2RY6 and PGBD1 rendered cells more sensitive to crizotinib in all SUP-M2, but not K299, cell lines (Figure 17B-C). A B C D E F 1 2 3 4 5 6 7 8 9 10 -5 0 5 A B C D E F 1 2 3 4 5 6 7 8 9 10 0 0.2 0.4 0.6 0.8 1.0 A B C D E F 1 2 3 4 5 6 7 8 9 10 -10 -5 0 5 10 A B C D E F 1 2 3 4 5 6 7 8 9 10 0.05 0.25 0.45 0.65 0.85 ALK+ ALCL ALK- ALCL A SH2D2A ADORA2A GPR161 HELZ2 IL10RA MKNK1 MYC P2RY6 PGBD1 RORC ALK+ ALCL ALK- ALCL B ALK+ ALCL ALK- ALCL C ALK+ ALCL ALK- ALCL D Log2(PRKM) Log2(PRKM)>1 & FDR<0.05Z-score <0.05 <0.001 FDR TRUE FALSE -5 0 5 -10 0 105-5 80 Figure 17 Knockout-based analysis (A) Schematic of the CRISPR-Cas9-based mini knockout screen for the identification of genes whose knockout modifies sensitivity to crizotinib in ALCL cell lines. A crizotinib/DMSO selection pressure is applied, and genomic DNA is harvested on day 0 and after 14 days of treatment. The sgRNA regions are amplified from genomic DNA and then analyzed by next-generation sequencing followed by statistical analyses. (B-C) Read counts of 6 sgRNAs targeting the indicated genes before and after a 14-day incubation with DMSO or (80 nM for SUP-M2, 100 nM for K299) crizotinib in the indicated ALCL cell lines. Data are represented as boxplot with individual points representing each sgRNA (n = 6). Unpaired t test: *p < 0.05. **p < 0.01, ***p < 0.001. RPM = Reads Per Million mapped reads. Reproduced from Prokoph et al.301. A crizotinib DMSO day 14 MYC/IL10RA/ADORA2A/GPR161/MKNK1/ PGBD1/SH2D2A/RORC/P2RY6/HELZ2 GeCKO v2 sgRNAs naïve ALCL cell line day 0 gDNA isolation & sgRNA amplification deep sequencing identification of depleted sgRNA sequences identification of depleted sgRNA sequences B SUP-M2 C 81 3.4.3 Validation of Candidate Genes Modulating ALK TKI Sensitivity in Resistant ALCL Cell Lines In parallel, the same 10 candidate genes were analyzed for their expression levels in cells that had been chronically exposed to ALK TKIs (crizotinib, alectinib, brigatinib or lorlatinib) to render them resistant, and were compared with transcript levels in parental (treatment naïve) cells (Figure 18). Among the genes assessed, IL10RA was overexpressed in 30% of resistant cell lines (4/12). This suggests that upregulation of IL10RA is a common mechanism of resistance in ALCL cell lines. Interestingly, overexpression of IL10RA was largely mutually exclusive with overexpression of NPM1-ALK (Figure 18). Figure 18 Hit validation in resistant ALCL cell lines Fold change expression of the indicated genes in crizotinib resistant (CR), alectinib resistant (AR), brigatinib resistant (BR) and lorlatinib resistant (LR) ALCL cell lines compared to parental cell lines. Genes with a fold change expression of > 2 were classified as overexpressed in resistant compared to parental cell lines. White indicates downregulated genes. Data are represented as means, n = 3. Reproduced from Prokoph et al.301. 3.4.4 Validation of Candidate Genes Modulating ALK TKI Sensitivity in a Resistant Orthotopic ALCL Cell Line Xenograft Model Next, we analyzed publically available RNA-seq data328 of lorlatinib-resistant tumours compared to vehicle control treated tumours from cell line-derived orthotopic xenografts (Figure 19A-B). HELZ2 and IL10RA were upregulated in 3 lorlatinib-resistant tumours compared to 3 vehicle control treated tumours from cell line-derived orthotopic xenografts (Figure 19B). Specifically, IL10RA was significantly upregulated in 3/10 lorlatinib-resistant tumours compared to 3 vehicle control treated tumours from cell line-derived orthotopic xenografts328 (Figure 19C). Fold change expression (relative to parental cells) IL10RA GPR161 ADORA2A P2RY6 MKNK1 RORC PGPD1 SH2D2A HELZ2 MYC NPM1-ALK 10 8 6 4 2 82 Figure 19 Hit validation in a resistant orthotopic ALCL cell line xenograft model (A) Schematic of orthotopic xenograft treatment. K299 cells were injected subcutaneously and vehicle control or lorlatinib treatment initiated once tumours reached an average size of 200 mm3. (B) Volcano plot summarizing the global changes in gene expression levels between lorlatinib resistant tumours (T1, T5, T7) and vehicle control treated tumours (V1-3). Red: genes enriched in the lorlatinib resistant tumours vs vehicle control treated tumours. Blue: genes depleted in the lorlatinib resistant tumours vs vehicle control treated tumours. (C) mRNA expression levels of IL10RA normalized to GAPDH and relative to K299 cells in lorlatinib resistant tumours (T1-T10) compared to vehicle control treated tumours (V1-3). b.i.d.: orally, twice a day. Data are represented as means ± SD of technical replicates, n = 3. Welch two sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. Reproduced from Prokoph et al.301. 3.4.5 Validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Patients To determine which targets identified by the screen are of potential clinical relevance, we analyzed data obtained from samples of resistant tumours from 4 patients with ALK+ ALCL recruited to the MAPPYACTS trial (NCT02613962), who had relapsed on ALK TKIs or chemotherapy (Figure 20A, Table 17). Patient 2 was treated with the standard ALCL99 chemotherapy protocol but progressed at 6 months following treatment initiation at which point crizotinib treatment was started, which only lasted for 2 months due to disease progression and then a biopsy was taken. Patient 1, having a more complex treatment history, had been treated with the ALCL99 chemotherapy protocol and remained in remission for 34 months until progression at which point multiple sequential therapies including crizotinib and lorlatinib were administered with short-term responses. The biopsy of this case was taken at the time of relapse on lorlatinib treatment while patients 3 and 4 were biopsied at the time of relapse from standard ALCL99 chemotherapy (Figure 20A). In contrast to patients 1 and 2, patients 3 and 4 responded to crizotinib and hence were classified as ALK TKI sensitive patients. In order to identify resistance drivers and associated pathways that might play a role in clinical resistance to ALK TKIs, we performed RNA-seq to compare gene expression profiles between ALK+ ALCL tumours with acquired resistance to ALK TKIs compared to those that relapsed on standard ALCL99 chemotherapy but were ALK TKI sensitive (Figure 20B-C). Using GSEA, we identified positive enrichment for autoimmune disease signaling pathways including autoimmune thyroid disease, type I diabetes mellitus, systemic lupus erythematosus and asthma pathways in the ALK TKI sensitive compared to the ALK TKI resistant patient tumours. As expected, the T cell receptor pathway was lorlatinib V 1 V 2 V 3 T1 T2 T3 T4 T5 T6 T7 T8 T9 T10 0 5 10 15 20 30 40 50 1 mg/kg b.i.d vehicle control Lorlatinib treatment 0.5 mg/kg b.i.d A Relapse/Analysis K299 Lorlatinib sensitive tumor Transplantation Remission Lorlatinib resistant tumor Treatment start (200 mm3) 2 mg/kg b.i.d IL 1 0 R A m R N A e x p re s s io n l e v e ls K299 ** ** * C B 83 enriched in ALK TKI sensitive patients381 (Figure 20D, Table 17). In agreement, the genes enriched in ALK TKI sensitive patients show specific gene ontology (GO) features such as T cell activation and differentiation (Figure 20E). Using GSEA, we identified positive enrichment for focal adhesion in ALK TKI resistant compared to ALK TKI sensitive patient tumours (Figure 20D). In agreement, the genes enriched in ALK TKI resistant patients showed GO features such as biological adhesion and cell adhesion (Figure 20E). Cell-cell adhesion is required for tissue invasion and metastasis of a tumour – one of the hallmarks of cancer251. Interestingly, Ott et al. developed a dual ALK and focal adhesion kinase (FAK) inhibitor, which they successfully validated in cell line xenografts of ALK+ ALCL and NSCLC382. Since FAK plays a role in cell-extracellular matrix signaling events as well as cell-cell junction regulation383, in future work FAK inhibitors could be tested in ALK inhibitor resistant settings. Notably, while the global gene expression profiles of both ALK TKI sensitive patients (patients 3 and 4) clustered together, the ALK TKI resistant patients (patients 1 and 2) did not (Figure 20F). To further investigate this difference in clustering, we determined whether any of the resistant patients had developed mutations in the ALK kinase domain. Patient 1 harbored a missense mutation (ALK L1196M, COSM99137) near the adenosine triphosphate (ATP)–binding pocket, which has previously been shown to mediate resistance to crizotinib in ALK+ NSCLC227, most likely accounting for the rapid relapse within 1 month of crizotinib initiation (Table 17, Figure 20A). In contrast, whilst we confirmed the presence of the NPM1-ALK rearrangement at crizotinib relapse for patient 2, no ALK mutation was detected. As expected, neither of the patients that relapsed after the standard ALCL99 chemotherapy (patients 3 and 4) had an ALK mutation at relapse (Table 17). These data allowed us to compare presumed ALK mutation-driven resistance (patient 1) to ALK mutation-independent resistance (patient 2). We therefore integrated our RNA-seq analysis with the CRISPR overexpression screen results (Figure 20G). Of the 10 candidate genes identified by the CRISPR screens, 6/10 genes (PGBD1, GPR161, HELZ2, MKNK1, IL10RA and RORC) were more highly expressed in the ALK wild-type tumour of patient 2 compared to the ALKL1196M tumour of patient 1 (Figure 20G). Of the 6 more highly expressed genes, IL10RA was selected for further analysis as it showed the highest expression levels among the 5 genes in tumours from both patients 1 and 2. 84 Figure 20 Validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Patients (A) Schema of the treatment history of ALK+ ALCL patients who relapsed on ALK targeted therapy (patients 1 and 2) or chemotherapy (patients 3 and 4). ALCL99* patient was treated according to ALCL99 recommendations for patients with central nervous system involvement as specified in Williams et al.99. (B) Unsupervised clustering of RNA-seq data from chemotherapy-relapsed (patient 3, patient 4) and ALK TKI resistant (patients 1 and 2) patients. CPM = counts per million. (C) Top panel: Venn diagram of genes unchanged (gray) or differentially expressed genes (blue/red) between chemotherapy-relapsed (patients 3 and 4) and ALK TKI (patients 1 and 2) resistant patients. Bottom panel: Volcano plot summarizing the global changes in gene expression levels between chemotherapy-relapsed (patients 3 and 4) and ALK TKI resistant (patients 1 and 2) patients. Red: genes enriched in the ALK TKI resistant patient vs chemotherapy-relapsed patients. Blue: genes depleted in the ALK TKI resistant patient vs chemotherapy-relapsed patients. An absolute log fold change of 1 is indicated by dashed lines. Reproduced from Prokoph et al.301. (figure continued on next page) A ALK TKI resistant ALCL patients A L K w t A L K L 1 1 9 6 M A L K w t A L K w t B patient 3 patient 4patient 1 patient 2 ALK TKI resistant CPM patient 1 patient 2 patient 3 patient 4 0 2 4 6 8 10 Chemotherapy- relapsed ALCL patients Chemotherapy-relapsed Log2 Fold Change -L o g 1 0 ( p -v a lu e ) -10 -5 0 5 10 0 5 15 10 no. of genes depleted in ALK TKI resistant patients no. of genes enriched in ALK TKI resistant patients 15428 142 135 no. of unchanged genesC 85 Figure 20 Validation of Candidate Genes Modulating ALK TKI Sensitivity in ALCL Patients (D) Gene ontology analysis of differential gene expression between chemotherapy-relapsed and ALK TKI resistant patients. BP = biological process; MF = molecular function. (E) Summary of GSEA of genes ranked by fold change in differential gene expression between chemotherapy-relapsed and ALK TKI resistant patients annotated KEGG pathways. (F) Principal component (PC) analysis of gene expression levels across the 4 ALCL patient samples. (G) Candidate genes identified by the CRISPR screens are analyzed for differential expression between ALK TKI resistant patients with wild-type or mutated ALK. CPM = counts per million. Reproduced from Prokoph et al.301. BP depletion in ALK TKI resistant patients BP enrichment in ALK TKI resistant patients MF depletion in ALK TKI resistant patients MF enrichment in ALK TKI resistant patients D E N o rm a liz e d E n ri c h m e n t S c o re Significant KEGG Pathways Enriched in ALK TKI resistant patientsDepleted in ALK TKI resistant patients G patient 2 ALKwt patient 1 ALKL1196M CPM ALK TKI resistant 7 6 5 4 3 2PC1: 48% P C 2 : 3 8 % Chemotherapy-relapsed F 86 3.5 Discussion In the relapse setting, patients with ALK-rearranged ALCL are commonly treated with ALK inhibitors including crizotinib63,117 and the second-generation inhibitors ceritinib384 and alectinib67. Two patient groups can be identified: Patients who i) achieved a CR and ii) present with disease progression during the first few months after treatment initiation. An understanding of the mechanisms leading to drug resistance in the second group is essential for designing therapeutic strategies to improve efficacy and prevent relapse. Previous work on ALK-rearranged NSCLC suggests that the most common mechanisms of resistance to ALK inhibition involve bypass signaling through functionally-related pathways385. Here we present findings from a systematic large-scale functional study of resistance to ALK inhibition in NPM1-ALK+ ALCL, with the aim to inform future therapeutic approaches that prevent relapse or provide salvage options. We conducted the screens in three cell lines to enhance physiological relevance and avoid fundamental caveats of specific cell line models, such as the distinct genetic backgrounds and consequently cell line-specific resistance mechanisms inherently present in each cell line model. Our CRISPR activation screens identified several new targets but were biased by the fold change gene expression achieved for a particular gene and sgRNA. While we have accounted for fold change gene expression in our validation process by excluding genes, which were not sufficiently overexpressed by either of the 2 tested sgRNAs (out of the 6 sgRNAs utilized in the CRISPR activation screens). We were not able to account for the level of overexpression due to the following reasons: Firstly, we do not know which level of overexpression for a specific gene – given that this gene can induce resistance - is sufficient to modify sensitivity to a drug. For example, P2RY6 was the most highly overexpressed gene in all 4 ALK+ ALCL cell lines, but its overexpression only induces resistance in K299. Secondly, if one accounts for the level of fold change gene expression, then genes that are more highly expressed than needed to modify sensitivity to a drug are discriminated against. To narrow down targets identified in the screen to those which may be clinically relevant, results were compared to RNA-seq data of relapse biopsy specimens of 2 ALCL patients resistant to ALK TKIs. A target identified by the screen was STAT3, a prime candidate being activated in inflammatory cells by a number of cytokines and previously shown to be central to ALCL biology downstream of NPM1-ALK activity225,365. Together with detection of the STAT3 target genes MYC and IRF4 by the screen, these data were suggestive of a potential NPM1-ALK bypass track which could be activated by a protein upstream of STAT3 and independent of NPM1-ALK. Indeed, IL10RA, a cell surface receptor usually upstream of STAT3, was also consistently detected and validated as mediating decreased sensitivity to crizotinib. Importantly, we also show that IL10RA overexpression decreases cell sensitivity to other ALK TKIs besides crizotinib including lorlatinib, alectinib and brigatinib. Given that IL10RA can mediate activation of STAT3 activity on ligand binding together with IL10RB, we further investigated the role of IL10RA in the following chapter. 87 CHAPTER 4 IL10RA Modulates ALK TKI Sensitivity in ALK+ ALCL 88 4.1 Introduction The Interleukin 10 receptor (IL10R) was discovered by Ho et al. in 1993 on the basis of its specific binding of interleukin 10 (IL10)386. IL10R is structurally related to the interferon receptor (INFR) family and is mainly expressed by hematopoietic cells including B cells, T cells, natural killer cells, monocytes, and macrophages386,387. In its functional form the IL10R is a tetramer, consisting of two IL10RA polypeptide chains and two interleukin 10 receptor subunit beta (IL10RB) chains387. Signaling most likely proceeds by JAK/STAT activation (Figure 21). Binding of the IL10 homodimer, to both extracellular domains of IL10RA, induces phosphorylation of janus kinase 1 (JAK1) and tyrosine kinase 2 (TYK2). Following this, two intracellular domain tyrosine residues (Y446 and Y496) in the IL10RA chains, act as temporary docking sites for the latent transcription factor STAT3. STAT3 is then phosphorylated by JAK1 and TYK2, and translocates to the nucleus, controlling the expression of downstream genes by binding STAT-binding-elements (SBE) motifs387–391. SBE motifs can be found in the promoter region of different genes388. Besides IL10-induced activation of STAT3, other STAT proteins have been reported to be involved in IL10 signaling, such as STAT1 and STAT5389,392. Figure 21 Schematic of the IL10 signalling pathway IL10 signaling has previously been shown to play a crucial role in ALCL. For example, IL10 benefits ALCL cells directly by enhancing their viability and indirectly by suppressing the immune response393. In addition, IL10 is already known to be one of the most abundant cytokines secreted by ALCL cell lines372 and is prevalent in the peripheral blood of children with ALCL394. Moreover, knockdown of IL10RA in the ALK+ cell line K299 resulted in reduced cell growth372, whereas the roles and functions of IL10RA in drug resistance have not yet been fully elucidated. In the previous chapter we show that overexpression of IL10RA, the IL10R-specific subunit, decreases sensitivity to ALK inhibitors in ALCL cell lines, whereas knockdown of IL10RA increases sensitivity to IL 1 0 R A IL 1 0 R A IL 1 0 R B IL 1 0 R B JAK1 JAK1 TYK2 TYK2 STAT3 P STAT3 P STAT3 P STAT3 P IL10 IL10 89 crizotinib in ALCL cell lines. These results were further confirmed in ALK inhibitor resistant cell lines and in a cell line-derived orthotopic xenograft model. Furthermore, RNA-seq performed on a relapse biopsy of an ALK inhibitor-resistant ALCL patient with wild-type NPM1-ALK showed higher expression of IL10RA compared to an ALK inhibitor-resistant ALCL patient that harbored an ALK L1196M mutation at relapse, suggesting these results may be translationally relevant. Hence, we further investigated the mechanism of how overexpression of IL10RA modifies sensitivity to ALK inhibition. The majority of the work presented in this chapter forms the basis of a publication in Blood (Prokoph et al.)301, which can be found in Appendix 1. 4.1.1 Aims This chapter aims to: • Determine the IL10RA expression levels in ALCL patient tumour tissue • Determine the effect of IL10RA overexpression on sensitivity to alectinib, brigatinib and lorlatinib • Determine the effect of IL10RA overexpression on apoptosis • Validate on-target effects of the CRISPR-based overexpression tool by reversing the resistant phenotype against IL10RA • Identify ALCL context-specific downstream effector(s) of IL10RA responsible for the inhibition of apoptosis in ALCL cells in response to ALK inhibtors • Determine whether IL10RA expression is predictive of clinical responses to standard chemotherapy in paediatric ALCL patients 4.2 IL10RA is Expressed in ALCL in an NPM1-ALK-Independent Manner The IL10R is a tetrameric cell surface protein composed of 2 A and 2 B subunits that bind IL10 leading to activation of JAK1/TYK2 and STAT3. To characterize the importance of IL10/IL10R signaling in ALCL, we measured the expression levels of IL10RA and IL10RB by immunostaining of T-cell lymphoma tissue microarrays (TMAs) from adult patients (Table 18, Table 19, Table 20). We determined that IL10RA was expressed in 100% of ALK+ ALCLs, 92% of ALK- ALCLs, 43% AITL and 30% of PTCL-NOS (Figure 22A-B). In contrast, IL10RB was in general expressed at a higher level in ALCL than other peripheral T cell lymphomas but there was no difference in the percentage of tumour cells positive for this protein (Figure 22B). We further confirmed IL10RA and IL10RB expression by immunostaining of TMAs80 comprising an independent cohort of 92 paediatric ALK+ ALCL patients (Figure 22C, Table 15, Table 13, Table 14) that were recruited onto three paediatric ALCL trials (NHL-BFM90/NHL- BFM95/NCT00006455). These data are consistent with existing gene expression data314–319 from 75 ALK+ ALCL compared to 40 ALK- ALCL patients, 160 PTCL-NOS patients, 100 AITL patients and 12 reactive lymph nodes from healthy donors (Figure 22D). 90 Figure 22 IL10RA is Expressed in ALCL Patient Tumour Tissue (A) Representative hematoxylin and eosin staining with corresponding IL10RA IHC staining performed on tissue microarrays from different human T cell lymphoma subtypes: ALK+ and ALK- ALCL, angioimmunoblastic T-cell lymphoma (AITL) and peripheral T-cell lymphoma not otherwise specified (PTCL-NOS). (B) Intensity of staining or percentage of tumour cells expressing IL10RA or IL10RB determined by IHC of a tissue microarray (TMA) of formalin fixed paraffin embedded (FFPE) T cell lymphoma patient samples: n (ALK+ ALCL) = 25, n (ALK- ALCL) = 25, n (AITL) = 7, n (PTCL-NOS) = 21. Data are represented as means ± SD. Welch two sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (C) Intensity of staining or percentage of tumour cells expressing IL10RA (n=92) or IL10RB (n=89) determined by IHC of a TMA of FFPE paediatric ALK+ ALCL patient samples. Data are presented as a violin plot with means indicated. Welch two sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (D) Microarray data (GSE58445, GSE6338, GSE65823, GSE78513, GSE14879, GSE19069) were analyzed for IL10, IL10RA and IL10RB expression in ALK+ ALCL (n = 75), ALK- ALCL (n = 45), PTCL-NOS (n = 160, peripheral T-cell lymphomas not otherwise specified), AITL (n = 100, angioimmunoblastic T-cell lymphomas) patients compared to reactive lymph nodes (LN, n = 12). Reproduced from Prokoph et al.301. To examine if IL10 signaling is represented in ALCL cell lines, we analyzed existing microarray data 316– 318,320,321 of a panel of lymphoma cell lines, including ALK+ and ALK- ALCL cell lines compared to other T- and B-cell lymphoma cell lines as well as normal T-cell controls (Figure 23A-B). In agreement with the patient data above, ALK+ ALCL cell lines showed robust mRNA expression of IL10RA, IL10RB and IL10. A IL10RA H&E ALCL, ALK+ ALCL, ALK- AITL PTCL-NOS A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S 0 25 50 75 100 P e rc e n ta g e A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S 0 25 50 75 100 P e rc e n ta g e ALCL, ALK+ ALCL, ALK- AITL PTCL-NOS A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S 0 1 2 3 IL 1 0 R B i n te n si ty A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S 0 1 2 3 IL 1 0 R A i n te n s it y *** **** B ** IL 1 0 R B s ta in in g i n te n s it y IL 1 0 R A s ta in in g i n te n s it y *** P e rc e n ta g e o f tu m o r c e lls w it h p o s it iv e I L 1 0 R A s ta in in g P e rc e n ta g e o f tu m o r c e lls w it h p o s it iv e I L 1 0 R B s ta in in g * * *** IL 10 R A IL 10 R B 0 25 50 75 100 P e rc e n ta g e IL10RA IL10RB IL 10 R A IL 10 R B 0 1 2 3 In te n s it y IL10RA IL10RB C S ta in in g i n te n s it y P e rc e n ta g e o f p o s it iv e ly s ta in e d t u m o r c e lls A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S re ac tiv e LN 0 5 10 15 IL10RB E x p re s s io n L e v e l A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S re ac tiv e LN 0 5 10 15 IL10RA E x p re s s io n L e v e l A LC L, A LK + A LC L, A LK - A IT L P TC L- N O S re ac tiv e LN 0 5 10 15 IL10 E x p re s s io n L e v e l D IL10 IL10RA IL10RB 91 Figure 23 IL10 signaling is represented in ALCL cell lines (A) Microarray data (GSE6338, GSE107951, GSE14879, GSE19069) were analyzed for IL10, IL10RA and IL10RB expression in ALK+ ALCL (n = 5) and ALK- ALCL (n = 2) cell lines compared to other lymphoma cell lines (n = 5), CD8+T cells, CD4+T cells, Treg cells, NK cells and NK T cells. (B) Microarray data (GSE94669) were analyzed for IL10, IL10RA and IL10RB expression in ALK+ ALCL (n = 7) and ALK- ALCL (n = 2) cell lines compared to other T cell (n = 3; H9, HuT78, HH) and B cell (n = 48) lymphoma cell lines. Reproduced from Prokoph et al.301. To determine whether IL10R signaling is mediated by NPM1-ALK, we examined whether IL10RA, IL10RB and IL10 expression were directly controlled by NPM1-ALK activity. Knockdown of ALK with an inducible shRNA led to decreased IL10, but not IL10RA nor IL10RB mRNA expression (Figure 24A), which is in agreement with existing microarray data322 of ALK shRNA transduced ALCL cell lines (Figure 24B). Consistent with this, crizotinib inhibition of NPM1-ALK activity also resulted in decreased IL10, but not IL10RA or IL10RB mRNA expression (Figure 24C). Again, our results are in agreement with existing microarray data322 from ALK TKI treated compared to untreated ALCL cell lines (Figure 24D). Hence, transcription of IL10RA is independent of NPM1-ALK expression and activity, suggesting IL10RA is a prime candidate for bypass signaling in response to ALK inhibition. D E L JB 6 K 29 9 S U -D H L- 1 S R 78 6 FE -P D M ac -2 A JU R K AT K E -3 7 M O LT -1 4 M O LT -3 P E E R C D 4+ T c el ls (r es tin g) C D 4+ T c el ls (a ct iv at ed ) Tr eg c el ls C D 8+ T c el ls (r es tin g) C D 8+ T c el ls (a ct iv at ed ) N K c el ls N K T ce lls 0 2 4 6 8 10 12 IL10RB E x p re s s io n L e v e l D E L JB 6 K 29 9 S U -D H L- 1 S R 78 6 FE -P D M ac -2 A JU R K AT K E -3 7 M O LT -1 4 M O LT -3 P E E R C D 4+ T c el ls (r es tin g) C D 4+ T c el ls (a ct iv at ed ) Tr eg c el ls C D 8+ T c el ls (r es tin g) C D 8+ T c el ls (a ct iv at ed ) N K c el ls N K T ce lls 0 2 4 6 8 10 12 IL10RA E x p re s s io n L e v e l D E L JB 6 K 29 9 S U -D H L- 1 S R 78 6 FE -P D M ac -2 A JU R K AT K E -3 7 M O LT -1 4 M O LT -3 P E E R C D 4+ T c el ls (r es tin g) C D 4+ T c el ls (a ct iv at ed ) Tr eg c el ls C D 8+ T c el ls (r es tin g) C D 8+ T c el ls (a ct iv at ed ) N K c el ls N K T ce lls 0 2 4 6 8 10 12 IL10 E x p re s s io n L e v e l controls ALK+ ALCL ALK- ALCL ALK- ALCL ALK+ ALCL ALK- ALCL controls A IL10 IL10RA ALK+ ALCL controls IL10RB controls S U P -M 2 TS K I-J K L8 2 JB 6 K 29 9 S U -D H L- 1 FE -P D M ac -1 T- ce ll N H L ce ll lin es B -c el l l ym ph om a ce ll lin es 0 2 4 6 8 10 IL10RA E x p re s s io n L e v e l S U P -M 2 TS K I-J K L8 2 JB 6 K 29 9 S U -D H L- 1 FE -P D M ac -1 T- ce ll N H L ce ll lin es B -c el l l ym ph om a ce ll lin es 0 2 4 6 8 10 IL10 E x p re s s io n L e v e l S U P -M 2 TS K I-J K L8 2 JB 6 K 29 9 S U -D H L- 1 FE -P D M ac -1 T- ce ll N H L ce ll lin es B -c el l l ym ph om a ce ll lin es 0 2 4 6 8 10 IL10RB E x p re s s io n L e v e l ALK- ALCL ALK+ ALCL B IL10 IL10RA IL10RB ALK- ALCL ALK+ ALCL ALK- ALCL ALK+ ALCLcontrols controls 92 Figure 24 Transcription of IL10RA is independent of NPM1-ALK expression and activity (A) Fold change in IL10, IL10RA and IL10RB mRNA expression levels in SUP-M2 derived TS or SU-DHL-1 cell lines transduced with a doxycycline dependent ALK shRNA. Doxycycline-induced cells were compared to non- induced cells and were normalized to GAPDH. Data are represented as means ± SD, n = 3. (B) Microarray data (GSE6184) were analyzed for IL10, IL10RA and IL10RB expression changes with or without ALK shRNA induction in SU-DHL-1 cells. Volcano plot summarizing the global changes in gene expression of ALK shRNA transduced ALCL cell lines with or without doxycycline induction. Red: downregulated, Dark gray: unchanged. (C) Fold change in IL10, IL10RA and IL10RB mRNA expression levels in crizotinib treated (300 nM for 6 hours) ALCL cell lines normalized to GAPDH and relative DMSO control. Data are represented as means ± SD, n = 3. (D) Microarray data (GSE6184) were analyzed for IL10, IL10RA and IL10RB expression changes after 6 hours treatment with 300 nM ALK inhibitor (CEP-14083) or DMSO treatment in SUP-M2 derived TS cells. Volcano plot summarizing the global changes in gene expression of SUP-M2 derived TS cells after 6 hours treatment with 300 nM CEP-14083 or DMSO. Red: downregulated, Dark gray: unchanged. Reproduced from Prokoph et al.301. 4.3 IL10RA Overexpression Modulates Sensitivity to ALK Inhibition To further characterize the effects of elevated IL10RA expression on ALK TKI induced cytotoxicity, we expressed 3 different sgRNAs activating IL10RA expression in 3 ALK+ ALCL cell lines (SU-DHL-1/SUP- M2/DEL) (Figure 25A). Expression of each sgRNA resulted in decreased crizotinib sensitivity especially on supplementation of IL10 (Figure 25C-E). As expected, the majority of these sgRNAs also promoted decreased sensitivity to the second generation ALK TKIs alectinib, brigatinib and lorlatinib. To investigate how IL10RA overexpression may be enabling cell survival in the presence of ALK TKIs, we assessed cell proliferation and apoptosis. None of the three IL10RA-targeting sgRNAs promoted proliferation in the absence of crizotinib (Figure 25B), suggesting that cell survival is not facilitated by increased proliferation. On the other hand, most IL10RA sgRNAs were able to prevent apoptosis to some extent when cells were treated with crizotinib (Figure 25F), all three IL10RA-targeting sgRNAs C IL 10 IL 10 R A IL 10 R B -20 -15 -10 -5 0 IL 10 IL 10 R A IL 10 R B -20 -15 -10 -5 0 IL10 IL10RA IL10RB N P M 1- A LK IL 10 IL 10 R A IL 10 R B -20 -15 -10 -5 0 A NPM1-ALK shRNA S U P -M 2 S U -D H L -1 F o ld C h a n g e E x p re s s io n (d o x -i n d u c e d r e la ti v e t o - n o n -i n d u c e d c e lls ) N P M 1- A LK IL 10 IL 10 R A IL 10 R B -25 -20 -15 -10 -5 0 NPM1-ALK IL10 IL10RA IL10RB Crizotinib S U P -M 2 S U -D H L -1 F o ld C h a n g e E x p re s s io n (r e la ti v e t o D M S O ) B NPM-ALK shRNA D SU-DHL-1 ALK inhibitor (CEP-14083) SUP-M2 Log2 Fold Change Log2 Fold Change -L o g 1 0 ( p a d j) -L o g 1 0 ( p a d j) -4 -2 0 2 4 0 2 4 6 -4 -2 0 2 4 0 1 2 3 93 demonstrated a significant ability to diminish apoptosis in the presence of crizotinib particularly when IL10 was supplemented to the growth media (Figure 25G). (legend on next page) S U -D H L- 1 S U P -M 2 D E L 0 50 100 150 500 1000 10000 20000 30000 F o ld C h a n g e E x p re s s io n (r e la ti ve t o N T s g R N A ) IL10RA sgRNA 49 IL10RA sgRNA 50 IL10RA sgRNA 72 1 2 3 4 0 2×108 4×108 6×108 NT sgRNA IL10RA sgRNA 49 IL10RA sgRNA 50 IL10RA sgRNA 72 1 2 3 4 0 2×108 4×108 6×108 NT sgRNA IL10RA sgRNA 49 IL10RA sgRNA 50 IL10RA sgRNA 72 1 2 3 4 0 2×108 4×108 6×108 NT sgRNA IL10RA sgRNA 49 IL10RA sgRNA 50 IL10RA sgRNA 72 15 678399. 80 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL10 15 678399. 80 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 15 678399. 80 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL101 5678399. 80 0 50 100 Brigatinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 15 678399. 80 0 50 100 Brigatinib [nM]N o rm a li z e d v ia b il it y [ % ] T sgRNA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL10 15 678399. 80 0 50 100 Brigatinib [nM]N o rm a li z e d v ia b il it y [ % ] sgRNA sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 12 5031 2 15 6 9. 80 0 50 100 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 12 5031 2 15 6 9. 80 0 50 100 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL10 12 5031 2 15 6 9. 80 0 50 100 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 [Crizotinib], (nM) N o rm a liz e d V ia b ili ty ( % ) A C SU-DHL-1 SUP-M2 DEL B Time, (Days) F lu o re s c e n c e ( 5 6 0 /5 9 0 n m ) [Alectinib], (nM) [Brigatinib], (nM) [Lorlatinib], (nM) SUP-M2 *** * ** * ** ** ** ** *** ** ** ** ** ** ** ** ** *** * ** * *** * ** ** *** *** *** *** ** ** *** *** * * *** *** ** ** 15 639 19 .59. 80 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL101 5639 19 .59. 80 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL1015 639 19 .59. 80 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 62 .5 41 .6 31 .2 5 15 .60 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL106 2. 5 41 .6 31 .2 5 15 .60 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL1062 .5 41 .6 31 .2 5 15 .60 0 50 100 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 62 578399. 80 0 50 100 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL106 2578399. 80 0 50 100 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL1062 578399. 80 0 50 100 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 25 0 12 5 62 .5 31 .2 50 0 50 100 Crizotinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 25 0 12 5 62 .5 31 .2 50 0 50 100 Crizotinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL10 25 0 12 5 62 .5 31 .2 50 0 50 100 Crizotinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 N o rm a liz e d V ia b ili ty ( % ) D [Crizotinib], (nM) [Alectinib], (nM) [Brigatini ], ( M) [Lorlatinib], (nM) SU-DHL-1 ** * * ** ** ** ** * * ** ** ** ** ** ** ** * ** * ** *** *** ** 94 Figure 25 IL10RA Overexpression Modulates Sensitivity to ALK Inhibition (A) Fold change in expression levels of IL10RA for each of the 3 sgRNAs targeting IL10RA versus non-targeting (NT) control sgRNA in the indicated ALCL cell lines. Data are represented as means ± SD, n = 3. (B) Proliferation of ALCL cell lines expressing sgRNAs inducing overexpression of IL10RA over 4 days (D1–D4). Data are represented as means ± SD, n = 3. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (C-E) Viability of (C) SUP-M2, (D) SU-DHL-1 or (E) DEL cells based on normalized CellTiter-Blue reads on exposure to increasing concentrations of crizotinib, alectinib, brigatinib or lorlatinib for 48 hours when expressing 1 of 3 of the indicated sgRNAs inducing overexpression of IL10RA in the presence or absence of 10 ng/mL IL10. Data are represented as means ± SD, n = 3. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (F) Modulation of apoptosis upon expression of sgRNAs inducing overexpression of IL10RA in the indicated ALCL cell line. The percentage of apoptotic cells is determined by annexin V and propidium iodide (PI) staining of ALCL cells treated with 125 (SU- DHL-1), 312.5 (SUP-M2) or 1250 (DEL) nM crizotinib for 48 hours. Data are represented as means ± SD of technical replicates, experiments performed independently three times. Welch two sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. Right panel: Representative flow cytometry plots of annexin V/PI staining intensities corresponding to IL10RA sgRNA promoting survival versus non-targeting (NT) control sgRNA in SUP-M2 cells. (G) Modulation of apoptotic response upon expression of sgRNAs inducing overexpression of IL10RA in the indicated ALCL cell lines. The percentage of apoptotic cells is determined by annexin V and propidium iodide (PI) staining of ALCL cells treated with 125 (SU-DHL-1), 312.5 (SUP-M2) or 1250 (DEL) nM crizotinib in the presence of 10 ng/mL IL10 for 48 hours. Data are represented as means ± SD of technical replicates, experiments performed independently three times. Welch two sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. Reproduced from Prokoph et al.301. Since these data hint towards the possibility that IL10RA-mediated crizotinib resistance is dependent on IL10, we next assessed whether IL10 overexpression alone could drive crizotinib resistance. Neither decreased crizotinib sensitivity nor a reduction in apoptosis was observed in ALK+ and ALK- ALCL cell lines when the growth media was supplemented with IL10 (Figure 26A-B). Consistent with this observation, overexpression of two different IL10-targeting sgRNAs in the same ALCL cell lines (Figure 26C) neither promoted proliferation in the absence of crizotinib (Figure 26D), nor increased survival in 50 00 31 5. 578399. 80 0 50 100 150 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL1050 00 31 5. 578399. 80 0 50 100 150 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] NT NT + IL10 50 50 + IL1050 00 31 5. 578399. 80 0 50 100 150 Lorlatinib [nM]N o rm a li z e d v ia b il it y [ % ] NT NT + IL10 49 49 + IL10 50 00 12 5031 3789. 80 0 50 100 150 Brigatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_72 IL10RA_72 + IL1050 00 12 5031 3789. 80 0 50 100 150 Brigatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_50 IL10RA_50 + IL1050 00 12 5031 3789. 80 0 50 100 150 Brigatinib [nM]N o rm a li z e d v ia b il it y [ % ] Control Control + IL10 IL10RA_49 IL10RA_49 + IL10 50 00 25 00 12 50 31 2. 5 9. 80 0 50 100 150 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 50 00 25 00 12 50 31 2. 5 9. 80 0 50 100 150 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL10 50 00 25 00 12 50 31 2. 5 9. 80 0 50 100 150 Alectinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 25 00 12 50 31 2. 5 9. 80 0 50 100 150 Crizotinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL102 50 0 12 50 31 2. 5 9. 80 0 50 100 150 Crizotinib [nM]N o rm a li z e d v ia b il it y [ % ] NT NA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL1025 00 12 50 31 2. 5 9. 80 0 50 100 150 Crizotinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 50 IL10RA sgRNA 50 + IL10 12 50 31 2. 5399. 80 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 49 IL10RA sgRNA 49 + IL10 [Crizotinib], (nM) [Alecti ib], (nM) [Brigatin b], (nM) [Lorlati ib], (nM) E DEL N o rm a liz e d V ia b ili ty ( % ) 50 00 25 00 12 50 31 2. 5 9. 80 0 50 100 150 A ectinib [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA NT sgRNA + IL10 IL10RA sgRNA 72 IL10RA sgRNA 72 + IL10 *** *** * *** *** *** *** ** *** * * ** * * * * * * ** ** * ** *** *** *** ** * * ** ** ** * ** ** ** *** *** **** * * * * SUP-M2 S U -D H L- 1 S U P -M 2 D E L 0 20 40 60 80 A p o p to s is ( % ) IL10RA sgRNA 49 IL10RA sgRNA 50 IL10RA sgRNA 72 NT sgRNA S U -D H L- 1 S U P -M 2 D E L 0 20 40 60 80 A p o p to s is ( % ) IL10RA sgRNA 49 IL10RA sgRNA 50 IL10RA sgRNA 72 NT sgRNA F Crizotinib + IL10 Q4 27.9% Q1 39.3% Q2 15.2% Q1 15.8% NT sgRNA IL10RA sgRNA 49 Q2 30.3% Q3 5.67% Q4 65.7% Q3 3.28% P I APC-Annexin V *** *** *** *** *** ** *** ** * ****** ** ** Crizotinib G 95 the presence of crizotinib (Figure 26E). This observation highlights the fact that expression of IL10RA is the limiting factor for IL10 signaling in these cell lines. (legend on next page) -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] PBS IL10 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] PBS IL10 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] PBS IL10 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] PBS IL10 -2 0 2 4 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] PBS IL10 S U -D H L- 1 S U P -M 2 D E L 0 10 20 30 40 + 10 ng/mL IL10 PBS control Log [Crizotinib], (nM) A N o rm a liz e d V ia b ili ty ( % ) P B S c o n tr o l Q4 38.1% Q1 26.8% Q1 37.0% Q2 26.3% B SU-DHL-1 SUP-M2 P I APC-Annexin V DEL Q2 32.1% Q3 2.97% Q4 30.6% Q3 6.19% Q1 15.1% Q2 38.8% Q4 29.9% Q3 16.2% Q1 29.0% Q2 29.8% Q4 38.6% Q3 2.62% Q1 33.6% Q2 26.7% Q4 31.8% Q3 7.88% Q1 12.1% Q2 36.1% Q4 30.7% Q3 21.0% + 1 0 n g /m L I L 1 0 H e a lt h y c e lls ( % ) n.s. n.s. n.s. K299, ALK+ DEL, ALK+SU-DHL-1, ALK+ SUP-M2, ALK+ Mac-2A, ALK- S U -D H L- 1 K 29 9 S U P -M 2 D E L M ac -2 A 0 2 4 50 100 500 1000 1500 F o ld C h a n g e E x p re s s io n (r e la ti ve t o N T s g R N A ) IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 1 2 3 4 0 2×108 4×108 6×108 N o rm a li z e d F lu o re s c e n c e (5 6 0 /5 9 0 n m ) NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 1 2 3 4 0 2×108 4×108 6×108 N o rm a li z e d F lu o re s c e n c e (5 6 0 /5 9 0 n m ) NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 1 2 3 4 0 2×108 4×108 6×108 N o rm a li z e d F lu o re s c e n c e (5 6 0 /5 9 0 n m ) NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 1 2 3 4 0 2×108 4×108 6×108 N o rm a li z e d F lu o re s c e n c e (5 6 0 /5 9 0 n m ) NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 1 2 3 4 0 2×108 4×108 6×108 N o rm a li z e d F lu o re s c e n c e (5 6 0 /5 9 0 n m ) NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] Control IL10_86 IL10_87 -2 0 2 4 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] Control 80 84 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] Control IL10_86 IL10_87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log(Crizotinib), nMN o rm a li z e d v ia b il it y [ % ] Control80 84 N o rm a liz e d F lu o re s c e n c e (5 6 0 /5 9 0 n m ) N o rm a liz e d V ia b ili ty ( % ) Time (Days) Log [Crizotinib], (nM) E K299, ALK+ DEL, ALK+SU-DHL-1, ALK+ SUP-M2, ALK+ Mac-2A, ALK- C F o ld C h a n g e E x p re s s io n (r e la ti v e t o N T s g R N A ) -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 D K299, ALK+ DEL, ALK+SU-DHL-1, ALK+ SUP-M2, ALK+ Mac-2A, ALK- -2 0 2 4 0 50 100 Log (Crizotinib) [nM]N o rm a li z e d v ia b il it y [ % ] NT sgRNA IL10 sgRNA 86 IL10 sgRNA 87 96 Figure 26 IL10 Overexpression does not Modulate Sensitivity to ALK Inhibition (A) Viability of the indicated ALCL cell lines based on normalized CellTiter-Blue fluorescence reads on exposure to increasing concentrations of crizotinib for 48 hours in the presence or absence of 10 ng/mL IL10. Data are represented as means ± SD of technical replicates, n = 3; experiment performed independently three times. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (B) Left panel: Representative distributions of annexin V and propidium iodide co-staining intensities of the indicated ALCL cells treated with 1250 (DEL), 312.5 (SUP-M2), 156.25 (SU-DHL-1) nM crizotinib with or without 10 ng/mL IL10 for 48 hours as determined by flow cytometry. Right panel: The percentage of healthy cells as determined by annexin V and propidium iodide (PI) staining of ALCL cells treated with 125 (SU-DHL-1), 312.5 (SUP-M2), 1250 (DEL) nM crizotinib with or without 10 ng/mL IL10 for 48 hr. Data are represented as means ± SD, n = 3. Welch two sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (C) Fold change in expression levels of IL10 modulated by CRISPR overexpression for two sgRNAs relative to non-targeting (NT) control sgRNA in the indicated ALCL cell lines. Data are represented as means ± SD of technical replicates, n = 3. (D) Proliferation of unchallenged ALCL cells expressing sgRNAs inducing overexpression of IL10. Proliferation was quantified over 4 days (D1–D4). Data are represented as means ± SD, n = 3. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (E) Viability of the indicated ALCL cell lines based on normalized CellTiter- Blue fluorescence reads on exposure to increasing concentrations of crizotinib for 48 hours when expressing 1 of 3 of the indicated sgRNAs inducing overexpression of IL10. Data are represented as means ± SD of technical replicates, n = 3; experiment performed independently three times. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. Reproduced from Prokoph et al.301. Next, we overexpressed IL10RA with a puromycin selectable plasmid311 and could confirm the results achieved with sgRNA mediated CRISPR overexpression (Figure 27A-D). Plasmid-based IL10RA overexpression was induced in ALCL cell lines (Figure 27A), which was able to desensitize ALCL cell lines to crizotinib treatment (Figure 27B) and rescue the phosphorylation of STAT3 in the presence of crizotinib (Figure 27D). Furthermore, the addition of the STAT3 inhibitor stattic395–397 resensitized IL10RA overexpressing cells to crizotinib inhibition (Figure 27C). Figure 27 Plasmid-based IL10RA Overexpression Modulates Sensitivity to Crizotinib Inhibition (A) Fold change in expression levels of IL10RA in the indicated ALCL cell lines after transfection with pLX302 IL10RA-V5 puro versus pLX302 control plasmid. Data are represented as means ± SD, n = 3. (B) Viability of ALCL cells based on normalized CellTiter-Blue fluorescence reads on exposure to 312.5 nM crizotinib for 48 hours when expressing pLX302 IL10RA-V5 puro versus pLX302 control plasmid. Data are represented as means ± SD, n = 3. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (C) Viability of ALCL cells based on normalized CellTiter-Blue fluorescence reads on exposure to 312.5 nM crizotinib and 2500 nM (SUP-M2) or 3000 nM (DEL) stattic for 48 hours when expressing pLX302 IL10RA-V5 puro versus pLX302 control plasmid. Data are represented as means ± SD, n = 3. Welch two-sample t test: *p < 0.05. **p < 0.01, ***p < 0.001. (D) Western blot analysis of differential JAK/STAT signaling activation in the indicated ALCL cells when expressing pLX302 IL10RA-V5 puro versus pLX302 control plasmid. Cells were treated with DMSO or 1000 nM crizotinib for 1 hour. This blot is representative of three independent experiments. Lines indicate different blots. Reproduced from Prokoph et al.301. S U P -M 2 D E L 0 20 40 60 80 100 N o rm a liz e d v ia b ili ty ( % ) pLX302 IL10RA-V5 puro pLX302 S U P -M 2 D E L 0 20 40 60 80 100 N o rm a liz e d v ia b ili ty ( % ) pLX302 IL10RA-V5 puro pLX302 A B C D Crizotinib p L X 3 0 2 p L X 3 0 2 I L 1 0 R A -V 5 p L X 3 0 2 p L X 3 0 2 I L 1 0 R A -V 5 pNPM-ALK NPM-ALK pSTAT3 STAT3 GAPDH DMSO SUP-M2 ** *** S U P -M 2 D E L 0 1000 2000 3000 50000 100000 150000 200000 IL 1 0 R A F o ld C h a n g e E x p re s s io n (r e la ti v e t o p L X 3 0 2 ) Crizotinib Crizotinib + Stattic Crizotinib p L X 3 0 2 p L X 3 0 2 I L 1 0 R A -V 5 p L X 3 0 2 p L X 3 0 2 I L 1 0 R A -V 5 DMSO DEL ** *** 97 4.4 Knockout of IL10RA/IL10RB/IL10 further sensitizes ALCL cells to ALK inhibition To understand if inhibition of any component of the IL10/IL10R complex would render cells sensitive to crizotinib treatment, we carried out a CRISPR-Cas9-based knockout of IL10, IL10RA and IL10RB in K299/SUP-M2 cells as described above using 6 sgRNAs per gene (Figure 17A) of which we validated 2 sgRNAs targeting IL10RA for their knockout efficiency (Figure 28B). We found sgRNAs targeting IL10, IL10RA and IL10RB significantly depleted in SUP-M2 cells that were treated with crizotinib for 14 days (D14 Crizotinib) in comparison to DMSO treatment (D14 DMSO) (Figure 28A). This is in agreement with a publicly available CRISPR knockout screen dataset by Ng et al.312 on 5 ALK+ and 1 ALK- ALCL cell lines that confirms that neither IL10RA, nor IL10 or IL10RB were found to be essential genes in the absence of ALK inhibition (Figure 28C-F).This suggests that the IL10R signaling pathway is not essential for the survival of ALCL cell lines, but becomes essential when ALCL cell lines are exposed to crizotinib. However, sgRNAs targeting IL10 were significantly depleted in K299 cells that were treated with crizotinib or DMSO in comparison to input control cells, suggesting that K299 cells are dependent on IL10 even without being challenged with crizotinib (Figure 28A). Moreover, in contrast to SUP-M2 cells, we did not find sgRNAs targeting IL10, IL10RA and IL10RB significantly depleted in K299 cells that were treated with crizotinib for 14 days (D14) in comparison to DMSO treatment (Figure 28A). 4.5 STAT3 is Activated Independently of NPM1-ALK through the IL10/IL10R Signaling Pathway on Crizotinib Inhibition We next explored the mechanism by which IL10RA mediates resistance to ALK inhibition. Oncogenic ALK-fusions activate several signaling pathways, with STAT3 representing a key downstream effector225,365. In agreement with previous publications395,398, ALK inhibition through crizotinib treatment led to a complete loss of STAT3 phosphorylation (Figure 29A). Activation of JAK/STAT signaling is also highly cytokine-dependent in lymphoid cells, with IL10 being a prominent activator373. To determine whether this also applies in ALCL, IL10RA overexpression was induced in ALCL cell lines using three different sgRNAs, which was able to rescue the phosphorylation of STAT3, but not STAT1, in the presence of crizotinib (Figure 29A). This indicates that IL10RA overexpression can mediate STAT3 phosphorylation independently of NPM1-ALK activity and that this mechanism can successfully reverse the effects of crizotinib-mediated inhibition on STAT3 activity. To understand how transcriptional targets of STAT3 are affected by IL10RA overexpression, we examined their expression levels by RT-qPCR. Consistent with this, overexpression of IL10RA led to increased mRNA levels of the known STAT3 target genes including MYC, IRF4 and CD30 in crizotinib- treated cells (Figure 29B). These data are in keeping with the CRISPR overexpression screen results whereby sgRNA mediated overexpression of both MYC and IRF4 enabled cell survival in the presence of crizotinib (Figure 12E). 98 Figure 28 CRISPR-based knockout of IL10RA/IL10RB/IL10 is not lethal, but sensitizes ALCL cell lines to ALK inhibition (A) Read counts of 6 sgRNAs targeting IL10/IL10RA/IL10RB before and after a 14-day incubation with DMSO or (80 nM for SUP-M2, 100 nM for K299) crizotinib in the indicated ALCL cell lines. Data are represented as boxplot with individual points representing each sgRNA (n = 6). Unpaired t test: *p < 0.05. **p < 0.01, ***p < 0.001. RPM = Reads Per Million mapped reads. (B) Western blot analysis of the indicated ALCL cell lines upon expression of sgRNAs inducing knockout of IL10RA versus non-targeting (NT) control sgRNA. Cells were harvested 7 days after infection. Tubulin was used as a loading control. This blot is representative of two independent experiments. Reproduced from Prokoph et al.301. (C-F) The CRISPR knockout screen dataset312 by Ng et al. identifies that IL10RA, IL10 and IL10RB are non essential genes in ALK+ and ALK- ALCL in the absence of ALK inhibition. MYC served as the positive control. (C) Genes ranked by Z-score. (D) Corresponding false-discovery rate (FDR) q- values. (E) Corresponding gene expression level; RPKM, reads per kilobase of transcript, per million mapped reads. (F) Combined gene expression level and FDR identifies vulnerabilities as true or false. A IL10RA Tubulin N T s g R N A IL 1 0 R A s g R N A 2 IL 1 0 R A s g R N A 4 SUP-M2 N T s g R N A IL 1 0 R A s g R N A 2 IL 1 0 R A s g R N A 4 IL10RA Tubulin K299B SUP-M2 IL10 IL10RA IL10RB *** ns ** ns *** ***ns *** ** D0 D14 DMSO D14 Crizotinib D0 D14 DMSO D14 Crizotinib D0 D14 DMSO D14 Crizotinib K299 IL10 IL10RA IL10RB ns ns * ns ns ns * *** ns D0 D14 DMSO D14 Crizotinib D0 D14 DMSO D14 Crizotinib D0 D14 DMSO D14 Crizotinib A IL10RA Tubulin N T s g R N A IL 1 0 R A s g R N A 2 IL 1 0 R A s g R N A 4 SUP-M2 N T s g R N A IL 1 0 R A s g R N A 2 IL 1 0 R A s g R N A 4 IL10RA Tubulin K299B A B C D E F 1 2 3 4 5 6 7 8 9 10 -10 -5 0 5 10 ALK+ ALCL ALK- ALCL C IL10RA IL10RB IL10 MYC ALK+ ALCL ALK- ALCL D ALK+ ALCL ALK- ALCL E ALK+ ALCL ALK- ALCL F <0.05 <0.001 TRUE FALSE A B C D E F 1 2 3 4 5 6 7 8 9 10 -5 0 5-5 0 5 -10 0 105-5 A B C D E F 1 2 3 4 5 6 7 8 9 0 -10 -5 0 5 10 A B C D E F 1 2 3 4 5 6 7 8 9 10 0.05 0.25 0.45 0.65 0.85 A B C D E F 1 2 3 4 5 6 7 8 9 0 0 0.2 0.4 0.6 0.8 1.0 A B C D E F 1 2 3 4 5 6 7 8 9 10 -5 0 5 Z-score FDR Log2(PRKM) Log2(PRKM)>1 & FDR<0.05 99 We also observed a strong correlation between IL10RA and IL10 mRNA expression levels across publicly available Human Protein Atlas RNA-seq datasets (Spearman ρ = 0.754, p < 1.68e-9) (Figure 29C) and an overexpression of IL10RA led to an increase in IL10 mRNA expression in crizotinib treated cells (Figure 29D). These results suggest that when IL10RA is expressed in ALK+ ALCL, it may function by creating an autocrine positive feedback loop via activation of STAT3. To investigate if STAT3 might directly regulate the transcription of IL10, IL10RA and IL10RB genes, we analyzed publicly available ChIP-seq data of two ALCL cell lines treated with crizotinib/DMSO304 and compared them to existing STAT3 ChIP-seq data on mouse CD4+ T cells313. We found STAT3 binding upstream of the TSSs of IL10/IL10RA/IL10RB in both ALCL cell lines (Figure 29E), but not in naïve CD4+ T cells (Figure 29F). Strikingly, when ALK activity was inhibited by crizotinib the STAT3 binding was abrogated (Figure 29E). In addition, using IRF4 as a positive control we validated several STAT3 peaks by ChIP followed by quantitative PCR (ChIP–qPCR) using a STAT3-specific antibody, confirming STAT3 binding to the TSSs of IL10/IL10RA/IL10RB in SUP-M2 cells (Figure 29G). Furthermore, we confirmed that IL10RA overexpression rescued STAT3 binding to the TSS of IL10/IL10RB/IRF4 in the presence of crizotinib (Figure 29H). Consistent with this, STAT3 depletion was found to diminish the expression of IL10 mRNA in ALCL cell lines expressing sgRNAs targeting IL10RA (Figure 29I). Thus, our data support a model whereby increased expression of IL10RA promotes upregulation of the IL10 ligand, ultimately reversing crizotinib-mediated inhibition of STAT3 phosphorylation. This mechanism promotes cellular survival and resistance to ALK TKI treatment in ALK+ ALCL (Figure 29J). 100 (legend on next page) IL 10 IL 10 R A IL 10 R B C trl IR F4 0.0 0.1 0.2 0.3 STAT3 GFP IL 10 IL 10 R A IL 10 R B C trl IR F4 0.0 0.1 0.2 0.3 GFP STAT3 0 5 10 15 20 DEL SUP-M2 SU-DHL-1 IL10RA sgRNA 49 IL10RA sgRNA 50 IL10 Fold Change Expression (relative to NT sgRNA) IL10RA sgRNA 72 0 2 4 6 CD30 IRF4 cMYC IL10RA sgRNA 49 IL10RA sgRNA 50 Fold Change Expression (relative to NT sgRNA) IL10RA sgRNA 72 Crizotinib N T s g R N A IL 1 0 R A s g R N A 4 9 IL 1 0 R A s g R N A 5 0 IL 1 0 R A s g R N A 7 2 pNPM-ALK NPM-ALK pSTAT3 STAT3 Tubulin N T s g R N A IL 1 0 R A s g R N A 4 9 IL 1 0 R A s g R N A 5 0 IL 1 0 R A s g R N A 7 2 pSTAT1 STAT1 A E B C Fold Change Expression (normalized to NT sgRNA) DMSO Crizotinib Input DMSO Crizotinib Input IL10 IL10RA IL10RB S U -D H L -1 J B 6 S T A T 3 C h IP -s e q D IL10 Fold Change Expression (normalized to NT sgRNA) DMSO chr1: 206,935-206,950 kb chr11: 117,850-117,875 kb chr21: 34,636-34,671 kb SUP-M2 DMSO % o f to ta l in p u t Crizotinib SUP-M2 p=1.68e−09 p=2.71e−08 IL 1 0 IL10RA G 2 4 6 CD30 IRF4 cMYC 49 IL10RA sgRNA 50 Fold Change Expression (relative to NT sgRNA) IL10RA sgRNA 72 -4 -2 0 2 4 6 -4 -2 0 2 ρ=0.754 F IL10 IL10RA IL10RB STAT3 ChIP H3K4me3 ChIP H3K4me3 ChIPSTAT3-deficient CD4+ T cells STAT3-WT CD4+ T cells chr1: 131,018-131,028 kb chr9: 45,252-45,272 kb chr16: 91,400-91,430 kb IL 10 IL 10 R A IL 10 R B C trl IR F4 0.0 0.1 0.2 0.3 STAT3 GFP IL 10 IL 10 R A IL 10 R B C trl IR F4 0.0 0.2 0.4 0.6 GFP STAT3 IL 10 IL 10 R A IL 10 R B C trl IR F4 0.0 0.1 0.2 0.3 GFP STAT3 IL 10 IL 10 R A IL 10 R B C trl IR F4 0.0 0.2 0.4 0.6 STAT3 GFP DEL pLX302 % o f to ta l in p u t DEL pLX302 IL10RA-V5 puro Crizotinib SUP-M2 pLX302 % o f to ta l in p u t SUP-M2 pLX302 IL10RA-V5 puro Crizotinib H IL 10 R A IL 10 R A IL 10 R B IL 10 R B JAK1 JAK1 TYK2 TYK2 NPM ALK IL-10 STAT3 target genes including IRF4 IL10RB IL10 STAT3 P STAT3 P STAT3 P STAT3 P IL10 IL10 ALKi IL 10 R B STAT3STAT3 IL 10 -10 -5 0 STAT3 shRNA 191 STAT3 shRNA 192 S TA T3 s hR N A 1 91 S TA T3 s hR N A 1 92 -10 -5 0 STAT3 shRNA 191 STAT3 shRNA 192 Fold Change Expression (relative to NT sgRNA) S TA T3 s hR N A 1 91 S TA T3 s hR N A 1 92 -20 -15 -10 -5 0 STAT3 shRNA 191 STAT3 shRNA 192 S TA T3 s hR N A 1 91 S TA T3 s hR N A 1 92 -20 -15 -10 -5 0 STAT3 shRNA 191 STAT3 shRNA 192 Fold Change Expression (relative to NT sgRNA) S TA T3 s hR N A 1 91 S TA T3 s hR N A 1 92 -15 -10 -5 0 STAT3 shRNA 191 STAT3 shRNA 192 S TA T3 s hR N A 1 91 S TA T3 s hR N A 1 92 -15 -10 -5 0 STAT3 shRNA 191 STAT3 shRNA 192 Fold Change Expression (relative to NT sgRNA) J SUP-M2 IL10RA sgRNA 50 SU-DHL-1 IL10RA sgRNA 72 I F o ld C h a n g e E x p re s s io n (n o rm a liz e d t o N T s h R N A ) S T A T 3 IL 1 0 DEL IL10RA sgRNA 72 F o ld C h a n g e E x p re s s io n (n o rm a liz e d t o N T s h R N A ) S T A T 3 IL 1 0 F o ld C h a n g e E x p re s s io n (n o rm a liz e d t o N T s h R N A ) S T A T 3 IL 1 0 101 Figure 29 STAT3 is Activated Independently of NPM1-ALK through the IL10/IL10R Signaling Pathway on Crizotinib Inhibition (A) Western blot analysis of differential JAK/STAT signaling activation in response to individual NT sgRNA control or IL10RA sgRNA overexpression in SUP-M2 cells treated with DMSO or 1000 nM crizotinib for 1 hour. This blot is representative of three independent experiments. Lines indicate different blots. (B) Fold change in transcript level of the indicated STAT3 target genes relative to GAPDH and relative to NT sgRNA in SUP-M2 cells expressing sgRNAs targeting IL10RA and treated with 1000 nM crizotinib for 1 hour. Data are represented as means ± SD, n = 3. (C) Correlation between IL10RA and IL10 mRNA expression levels in the Human Protein Atlas RNA-seq datasets, including non-transformed (red) and cancer (gray) cell lines. ρ, Spearman correlation coefficient. (D) Fold change in IL10 mRNA expression levels in crizotinib treated ALCL cell lines expressing sgRNAs inducing expression of IL10RA. Data are represented as means ± SD, n = 3. (E) STAT3 ChIP-seq tracks near the IL10/IL10RB/IL10RA loci in ALCL cell lines treated for 3 hours with crizotinib (300 nM) or DMSO. (F) STAT3 ChIP– seq validation by ChIP–qPCR of the IL10/IL10RA/IL10RB and IRF4 TSS in SUP-M2 cells treated for 3 hours with crizotinib (1000 nM) or DMSO. Data are represented as means ± SD of technical replicates; experiment was performed independently three times. IRF4 served as a positive control. (G) STAT3 and H3K4me3 ChIP-seq tracks near the IL10, IL10RA and IL10RB loci in STAT3 wild type (WT) or STAT3-deficient mouse CD4+CD44-CD62L+ T cells. (H) STAT3 ChIP–qPCR of the IL10/IL10RA/IL10RB and IRF4 TSS in the indicated ALCL cell lines when expressing pLX302 IL10RA-V5 puro versus pLX302 control plasmid treated for 3 hours with crizotinib (1000 nM). Data are represented as means ± SD of technical replicates; experiment was performed independently three times. (I) Fold change in expression levels of STAT3 and IL10 on STAT3 shRNA induction in the indicated ALCL cell lines compared to non-targeting (NT) control shRNA and simultaneous expression of sgRNAs inducing overexpression of IL10RA. Data are represented as means ± SD, n = 3. (J) Model summarizing the mechanism by which IL10RA overexpression leads to ALK TKI resistance. Reproduced from Prokoph et al.301. 4.6 High Expression of IL10RA at Diagnosis is not Predictive of Clinical Outcome for Patients Treated with Standard Chemotherapy To determine whether IL10RA is an ALK TKI-specific resistance driver in ALK+ ALCL, we evaluated IL10RA protein expression levels in ALK+ ALCL patients treated with standard ALCL99 chemotherapy (n = 97, Table 15, Table 13, Table 14). To determine whether high IL10RA protein expression levels at diagnosis confer chemotherapy resistance, we analyzed IL10RA expression levels in tumour samples collected before treatment initiation, and divided chemotherapy-treated patients into “relapse” and “no relapse” cases. Patients who showed no evidence of disease for over 10 years after chemotherapy were classified as “no relapse” cases and patients with disease recurrence within 10 years were considered “relapse” cases (Figure 30A). Samples from cancer patients who relapsed after standard ALCL99 chemotherapy did not show significantly higher IL10RA protein expression levels at diagnosis compared to patients that remained in remission (Figure 30B). Furthermore, IL10RA expression had no influence on EFS or OS in patients treated with chemotherapy (Figure 30C-F). Collectively, these results indicate that IL10RA expression does not correlate with response or resistance to standard ALCL99 chemotherapy. Whether IL10RA overexpression because of ALK TKI therapy re-sensitizes tumour cells to chemotherapy, and furthermore if co-treatment with an ALK TKI and chemotherapy could overcome resistance remains to be determined. 102 Figure 30 Initial High Expression of IL10RA is not Predictive of Clinical Outcome for Patients Treated with Chemotherapy (A) Schematic summary of diagnostic biopsy specimens of ALK+ ALCL patient tumours analyzed by IHC. Numbers of standard ALCL99 chemotherapy treated patients that presented with a “relapse” or “no relapse” are indicated below each chart. (B) Percentage of tumour cells expressing IL10RA in diagnostic biopsy specimens of patients presented in (A) (n = 98) that were treated with standard ALCL99 chemotherapy. Individual quantifications are plotted with means ± SD indicated. (C,D) Paediatric patients (n = 92) treated with standard ALCL99 chemotherapy as part of the NHL-BFM90, NHL-BFM95 and ALCL99 trials were divided into two groups (low < 50%, high >= 50%) according to the percentage of tumour cells expressing IL10RA and the difference in median (C) EFS or (D) OS (log-rank test) was analyzed using the Kaplan–Meier estimator. P value determined by Cox proportional HR and the 95% CI is shown. pts, patients. (E,F) Forest plot assessing the effects of the indicated clinical parameter on (E) EFS or (F) OS. P values determined by Cox proportional HR with 95% CI are shown. Reproduced from Prokoph et al.301. C no relapse relapse 0 25 50 75 100 IL 1 0 R A s ta in in g p e rc e n ta g e (n = 64) (n = 34) ns Chemotherapy (CT) relapse n = 34 cases no relapse n = 64 cases Chemotherapy (CT) E F S p ro b a b ili ty o f A L C L p a ti e n ts o n C T Time (years) IL10RA percentage low IL10RA percentage high No. at risk Legend Pediatric ALCL patients Adult ALCL patients P e rc e n ta g e o f tu m o r c e lls w it h p o s it iv e I L 1 0 R A s ta in in g lapse rela A B D Time (years) O S p ro b a b ili ty o f A L C L p a ti e n ts o n C T IL10RA percentage high IL10RA percentage low No. at risk E F 103 4.7 Discussion Among PTCLs, ALCL has been associated with the highest level of IL10 expression399. In addition, IL10 together with IL22 are known to be the most abundant cytokines secreted by ALCL cell lines372 and their expression is mediated by NPM1-ALK393,400. Both cytokines form autocrine loops to activate the IL10R (IL10RA/IL10RB) and the IL22R (IL22RA1 or IL22RA2/IL10RB)401, respectively, that ultimately mediate a pro-proliferative effect via JAK/STAT signaling pathway activation393,400. Although this illustrates that both cytokines and their receptors play a pivotal role in ALCL, only IL10RA, the IL10R specific subunit, was detected in the CRISPR overexpression screen. Furthermore, our study provides evidence that IL10RA and IL10RB expression are independent of NPM1-ALK expression, while IL22RA1 expression has been shown to be induced by NPM1-ALK400. Thus, we reasoned that IL10R subunits can be highly expressed even in the presence of crizotinib-mediated ALK inhibition, representing a bypass signaling pathway. In future work it will be interesting to investigate how IL10RA overexpression is achieved. This could be either through decreased recycling of IL10RA or its increased transcription, perhaps driven by a transcription factor such as CEBPB, whose activity is not affected by ALK inhibition304. However, it is also possible that tumour cell sub-clones with higher IL10RA expression levels already exist and are selected with ALK TKI therapy. Interestingly, two lorlatinib-resistant samples carrying an ALK L1196M mutation (patient 1 and mouse xenograft T5) showed high levels of IL10RA expression, suggesting that IL10 signaling might cooperate with mutant NPM1-ALK to provide a drug resistance phenotype, thus allowing expansion of an otherwise lorlatinib-sensitive NPM1-ALK mutant tumour. IL10 binding results in autophosphorylation of the IL10RA subunit, which in turn leads to the activation of Janus kinase 1 (JAK1) or non-receptor tyrosine-protein kinase (TYK2). The activation of these two kinases further gives rise to the downstream activation of STAT family members402, with IL10 preferentially signaling via STAT3393. Our data demonstrate that crizotinib inactivates STAT3 signaling by inhibiting NPM1-ALK-induced phosphorylation, whereas IL10RA expression leads to phosphorylation of STAT3 accounting for renewed signal transduction downstream of STAT3. Therefore, STAT3, pan- JAK or TYK2 inhibitors are rational candidates for combination with ALK TKIs to overcome or prevent therapy resistance372,403. Although targeting STAT3 has proven difficult404, the STAT3 antisense oligonucleotide AZD9150 may potentially provide another effective option405. Alternatively, several pan- JAK or TYK2 inhibitors have been successfully validated in ALCL cell lines as efficacious single agents372. Furthermore, our results indicate that IL10RA expression does not correlate with response or resistance to standard chemotherapy, suggesting that resistance mechanisms, such as elevated IL10RA expression developing as a consequence of single agent crizotinib therapy, could be overcome by a combination of ALK-targeted therapy with chemotherapy. Hence, a combination of crizotinib with chemotherapy could prevent ALK-inhibitor resistance-specific relapse. 104 CHAPTER 5 Brigatinib is effective in a PDX of crizotinib-resistant ALK+ ALCL 105 5.1 Introduction The relapse rate for paediatric ALK+ ALCL reaches 50% independent of the chemotherapy regimen20,52,72,406,407. Unfortunately, shorter time to relapse is the strongest predictor for a subsequent relapse with approximately 50% of children who had progression during frontline therapy experiencing progression again during reinduction98. In addition, three-year OS for patients after CNS relapse is 48.7%100. The rarity of the disease combined with the fact that re-biopsy at relapse is not a routine procedure has meant that genomic and expression analysis of relapse and refractory ALK+ ALCL has not been extensively conducted. Beyond a recent study by Lobello et al., which compared tumour samples at diagnosis versus relapse in 4 adult patients and identified TP53 as well as EPHA5 mutated clones as possible drivers of relapse84,408, little knowledge exists regarding the drivers of chemo-relapse in paediatric ALK+ ALCL. The ALK inhibitor crizotinib has been trialled as a salvage therapy in paediatric ALK+ ALCL patients that relapsed from chemotherapy63,64,66,70,117, but preliminary results from the AcSé CRIZOTINIB trial showed 5/15 patients progressed126. Until now fewer than 130 paediatric ALK+ ALCL patients (NCT01979536, n = 103; NCT02034981, n = 11; UMIN000028075, n = 10) have been treated with crizotinib in a clinical trial setting. None of the 103 patients recruited to NCT01979536 have been re-biopsied at relapse due to ethical constraints and/or the health status of the patient. Of the 11 paediatric patients recruited to NCT02034981, several remained in complete remission or went on to receive a SCT and were therefore not re-biopsied126. Therefore, our current knowledge of ALK-dependent resistance mechanisms is based so far on just 4 patients. Gambacorti Passerini et al. amplified the kinase domain of NPM1-ALK from peripheral blood samples from two adult ALK+ ALCL patients and identified the presence of ALKQ1064R, ALKI1171N and ALKM1328I through deep sequencing after crizotinib relapse128. In addition, in section 3.4.5, we recently identified an ALKL1196M mutation by WES of tumour tissue from a crizotinib and lorlatinib resistant paediatric ALK+ ALCL patient, while a further crizotinib resistant paediatric ALK+ ALCL patient did not have an ALK mutation nor a NPM1-ALK amplification hinting towards the possibility of an existing bypass resistance mechanism301. Beside those three studies, mechanistic investigations into treatment regimens for relapsed disease have been focused on cell line-based models mostly established from tumour cells obtained from the diagnostic biopsy of patients (COST, DEL, Ki-JK, SU-DHL-1)257 and cell lines or cell line xenografts chronically exposed to ALK TKIs to render them resistant217,301,326–328,358,371,395,409,410–412. However, in vitro culture conditions may cause rapid phenotypic and genotypic divergence of patient-derived cells from the originating tumour413, and mouse xenografts utilising these cell lines have demonstrated limited predictive power in translational research414,415. PDX models have evolved as powerful pre-clinical tools; by maintaining the heterogeneity of patient tumours, PDX models allow for more clinically-relevant insights into responses to treatment and development of therapy resistance416. For instance, the Paediatric Preclinical Testing Program of the National Cancer Institute has shown improved prediction of clinical response with PDX models as opposed to cell line xenografts417. Immunodeficient mice including athymic nude mice, severe combined immunodeficiency (SCID), nonobese diabetic (NOD)- SCID, and recombination-activating gene 2 (Rag2)-knockout mice have been used to establish xenograft models418. The use of NOD/SCID mice with interleukin-2 receptor subunit gamma (IL2RG) 106 mutations (NSG) has proven to be effective across a range of cancers including those of lymphoid origin413. However, the replacement of human stromal components by murine elements as well as the lack of interaction between immune cells and tumour cells are major disadvantages249. Although transgenic mouse models would provide these tumour-stroma-immune interactions, they are not of human origin413. In addition, the development of genetically engineered mouse models representing ALK+ ALCL has been challenging. Transgenic murine models which express NPM1-ALK driven by vav419/CD2420 promoters or conditionally express ALK using the tetracycline system driven by the EμSRα promoter421 developed B-cell lymphomas.. Chiarle et al. developed a murine model which expressed NPM1-ALK under the CD4 promoter, therefore restricting NPM1-ALK to T cells422. This model still did not fully mimic ALK+ ALCL, with largely thymic-restricted tumours although cells did express CD30422. A cre-lck promoter chimeric model produced thymic T-cell lymphomas with CD30 expression423,424. However, this model relies on ex vivo retroviral transduction; accordingly new mice have to be generated for each study with this model424. The closest ALCL mimic to date was developed in the Turner lab again expressing NPM1-ALK from the T cell specific CD4 promoter, but backcrossed to the class I-restricted Ova-specific T-cell receptor (TCR) transgenic line OT1425. As in human ALCL, tumours arising in these mice lack cell surface expression of the TCR complex. However, NPM1-ALK is expressed at all stages of thymocyte development and is therefore not exclusive to CD4 single positive T cells. In addition, tumours arising in these mice variably express CD4, CD8 or CD4 in combination with CD8425. Therefore, until the use of humanized PDX models becomes cost-effective, the use of PDXs in NSG mice offers the best platform for discovery and testing of targeted therapies for tumours showing poor responses to multi-agent chemotherapy and allogenic SCT. The first PDX model of paediatric ALK+ ALCL was developed by Kadin and colleagues256. Here we add to this by developing a unique PDX and cell line resource from ALK+ ALCL patients at or before CNS relapse compromising a subgroup of patients with unmet clinical need where no models currently exist to the best of our knowledge256,312. 5.1.1 Aims This chapter aims to: • Establish PDXs of liquid biopsy samples obtained from crizotinib resistant and chemotherapy relapsed/refractory ALCL patients. • Establish cell lines from the PDX tumours • Determine whether the PDX model that was established from a crizotinib resistant patient maintains its crizotinib resistance in vivo • Determine whether the crizotinib resistant PDX model and/or cell line is sensitive to second generation ALK inhibitors 107 5.2 Patient treatment history and sample collection Two paediatric ALK+ ALCL patient experienced relapse/refractory disease during frontline ALCL99 chemotherapy (Figure 31A, Table 16). Patient 1 further progressed with CNS involvement on vinblastine treatment combined with intravenous and intrathecal chemotherapy. This is in line with a previous publication, which reported that shorter time to relapse was the strongest predictor of subsequent relapse98. Since this treatment was poorly tolerated and only an incomplete response achieved, the patient commenced crizotinib treatment alongside intrathecal chemotherapy, achieving a complete remission (CR). The patient then received an allogenic SCT but progressed rapidly thereafter. The patient was re-treated with crizotinib until eventual progression, at which time we isolated mononuclear cells (MCs) from a bone marrow sample and injected them subcutaneously into NSG mice (Figure 31B, MGS-A-x). In line with a previous publication, which reported a three-year OS after CNS relapse of 48.70% for paediatric ALK+ ALCL patients100, the patient sadly passed away 13 months after diagnosis. Patient 2 commenced crizotinib treatment with intrathecal chemotherapy due to continued refractory disease, despite treatment including ALCL99 chemotherapy (Figure 31A, Table 16). We isolated MCs from a pleural effusion sample obtained early in the disease course, before crizotinib initiation, and injected them subcutaneously into NSG mice to establish a PDX model (Figure 31B, MTK-A-x). Unfortunately, despite excellent initial response to crizotinib, with CR confirmed on imaging seven weeks after initiation, Patient 2 relapsed with aggressive isolated CNS involvement shortly afterwards and sadly died despite further intensive conventional intravenous chemotherapy (Figure 31A). 5.3 Brigatinib is effective in a PDX of crizotinib-resistant ALK+ ALCL Tumours were established in the mice from the MCs within 3 months and were confirmed by IHC to be positive for ALK and CD30 expression (Figure 31C). Next, to test the response of PDX tumours to ALK inhibitors in a high-throughput manner, we established cell lines from the PDX tumours of patients 1 (MGS) and 2 (MTK) (Figure 31B). In line with the clinical characteristics of the patients (Figure 31A, Table 16), MGS was less sensitive to crizotinib as compared to MTK (Figure 31D). In addition, while MGS was also less sensitive to ceritinib, the second generation ALK inhibitors alectinib, brigatinib and lorlatinib were effective (Figure 31D). 108 Figure 31 Established cell lines maintain crizotinib responsiveness of the original tumour (A) Schema of the treatment history of the ALK+ ALCL patient with refractory disease on ALCL99 chemotherapy (Patient 2) or on ALCL99 chemotherapy and ALK targeted therapy (Patient 1). BV = Brentuximab vedotin, VBL = vinblastine. See Table 16 for further patient details. (B) Schema of the PDX and cell line generation. Mononuclear cells (MCs) were isolated from bone marrow (Patient 1) or pleural effusion (Patient 2) samples and injected subcutaneously into NSG mice to establish a PDX model of ALK+ ALCL. Cell lines were established from the PDX tumours. (C) Representative haematoxylin and eosin staining (400×) with corresponding ALK and CD30 IHC (400×) performed on sections of the diagnostic tumour compared with the PDX tumour (passage ≤3) and the corresponding established cell line (passage ≤10). (D) Viability of indicated cell lines based on normalized CellTiter-Blue fluorescence reads on exposure to increasing concentrations of ALK inhibitors for 48 hours. Data are means ± SD of technical replicates. Reproduced from Prokoph & Matthews et al. (unpublished). 109 Since brigatinib is due to be tested in the next EICNHL trial (personal communication with Dr. Suzanne Turner) and crizotinib has been trialled63,64,66,70,117 in paediatric ALK+ ALCL patients that relapsed from chemotherapy (NCT00939770, NCT01606878, NCT01979536, NCT02304809, UMIN000028075, ITCC053) we selected the two ALK inhibitors for in vivo investigation (Figure 32). Tumour-bearing MGS-A-x NSG mice were treated daily by oral gavage with either vehicle (PBS, 10% DMSO), crizotinib (100 mg/kg), or brigatinib (25 mg/kg). To better simulate an advanced disease stage, we started treatment when tumours reached 400 mm3 in volume. The ALK inhibitor concentrations used were based on the findings of previous in vivo studies carried out by the European Medicines Agency 426. The crizotinib dose used converts to a human equivalent dose of 301,8 mg/m2 using conversion formulars based on Freireich et al.427 and assuming a child weight of 20 kg and a mouse weight of 0.033 kg, as recommended by the FDA428. This dose is comparable to 165 mg/m2 used in the ongoing AcSé trial126 (NCT02034981), COG-ANHL12P1 trial (NCT01979536) and the completed COG-ADVL0912 trial (NCT00939770). Mice were euthanized once tumours reached 15 mm in any direction. The study was stopped after 21 days of consecutive treatment. Brigatinib led to a reduction in the mean tumour volume compared to the baseline level, and relative to either vehicle or crizotinib treatment (Figure 33A). While 5/8 mice that were treated with brigatinib showed a CR, 7/8 mice that were treated with crizotinib presented with tumour progression (Figure 33B). Survival analysis showed a significant increase in EFS for animals treated with brigatinib relative to vehicle (HR 0.07, p = 0.0179), but not for animals treated with crizotinib relative to vehicle (HR 1.09, p = 0.882) (Figure 33C), where an event was defined as a tumour reaching 15 mm in any one direction. Brigatinib was well-tolerated, with no significant decrease in body weight or lethal toxicity observed compared to either vehicle (p = 0.4263) or crizotinib (p = 0.6407, Figure 33D). 5.4 Discussion There are two ways in which PDX models can be used to investigate treatment-resistant cancer: (i) PDX models can be derived from patient samples at the time of treatment resistance or (ii) PDX models can be developed from pretreatment tumour samples and resistance can be modelled in the PDX via artificial exposure to the drug429. Here we successfully established two PDX models from liquid biopsies of multi- agent chemotherapy-refractory (Patient 2, MTK-A-x), and both multi-agent chemotherapy-refractory and crizotinib resistant (Patient 1, MGS-A-x) paediatric ALK+ ALCL patients. PDX models have been shown to retain the drug-sensitivity of the engrafted patient tumour, although PDX models of melanoma430,431 and lung adenocarcinoma242 became sensitive after xenografting due to the imposed 'drug holiday'. While this may suggest that treatment-resistant PDXs should be propagated under the continuous selective pressure of treatment, this might on the other hand lead to the selection of subclones ultimately resulting in genetic differences between the PDX tumour and the original patient tumour429. Here, we show that although the MGS-A-x PDX model was established under a 'drug holiday' it retained the crizotinib-resistance profile of the corresponding patient tumour. 110 Figure 32 Tumour volume over time in MGS-A-x PDX mice Tumour volume over time in MGS-A-x PDX mice administered with brigatinib (25 mg/kg; n = 8; mice D1-D8), crizotinib (100 mg/kg; n = 8; ,mice E1-8) or vehicle (PBS, 10% DMSO; n = 8; mice F1-F8) daily by oral gavage once tumours reached 400 mm3 in volume. Tumours were measured daily with manual calipers and tumour volumes estimated using the modified ellipsoid formula: V = ab2/2, where a and b (a > b) are length and width measurements. Mice were euthanized once tumours reached the ethical limit of 15 mm in any direction. The study was stopped after 21 days of consecutive treatment. Mice were censored (*) due to tumour ulceration (mice E2, F5), sudden death (mice D3), self-mutilation (E8), sickness (D1) or if mice remained tumour-free after 21 days of consecutive treatment (D4-8). Modified from Prokoph & Matthews et al. (unpublished). 111 Figure 33 Brigatinib is effective in the treatment of a PDX of crizotinib-resistant ALK+ ALCL (A) Tumour volume over time in MGS-A-x PDX mice administered with vehicle (PBS, 10% DMSO; n = 8), crizotinib (100 mg/kg; n = 8) or brigatinib (25 mg/kg; n = 8) daily by oral gavage once tumours reached 400 mm3 in volume. Tumours were measured daily with manual calipers and tumour volumes estimated using the modified ellipsoid formula: V = ab2/2, where a and b (a > b) are length and width measurements. Mice were euthanised once tumours reached 15 mm in any direction. The study was stopped after 21 days of consecutive treatment. Data points represent mean ± SEM. (B) Percentage change of the tumour volume at the study endpoint against the baseline for individual tumour-bearing MGS-A-x mice (n = 8) represented as bars according to each treatment specified in (A). The study end point was reached once tumours reached 15 mm diameter in any direction or after 21 days of consecutive treatment. (C) Kaplan–Meier event-free survival according to each treatment group specified in (A) for tumour-bearing MGS-A-x mice, where survival is defined as the time taken for tumours to reach 15 mm diameter. The study endpoint was reached once tumours reached 15 mm diameter in any direction or after 21 days of consecutive treatment. P value determined by Cox proportional HR: Brigatinib vs vehicle (HR 0.07, p = 0.0179), crizotinib vs vehicle (HR 1.09, p = 0.882). See Figure 32 for MGS-A-x PDX mice that were censored. (D) MGS-A-x mouse body weight at the experiment end-point relative to the baseline per treatment group specified in (A). Data are represented as box plots with individual points representing each mouse (n = 8). P-values were determined by two-sample t-test. Modified from Prokoph & Matthews et al. (unpublished). We utilised immunodeficient NSG mice for PDX establishment. Despite the importance of an absent immune system to enable tumour engraftment, this is also one of the major limitations of NSG mouse models given the important role the tumour microenvironment (TME) plays in cancer. It will be necessary for ALK+ ALCL PDX models to possess a human immune system to facilitate the study of immune- cancer cell interactions and preclinical assessment of cancer immune therapies for the following reasons. Firstly, the anti-CD30 antibody armed with the antimicrotubule agent monomethyl auristatin E (MMAE) - BV – may be introduced as a frontline treatment either in combination with crizotinib (NCT02729961) or with the ALCL99 chemotherapy backbone (NCT01979536). Both multi-agent chemotherapy432,433,434,435 and ALK inhibitors436,437 have been shown to have immune stimulatory potential. Secondly, Nivolumab is being investigated as a treatment option for ALK+ ALCL patients that have failed both chemotherapy and ALK inhibitor treatment (NCT03703050). 112 Humanized mice are immunocompromised mice in which a competent human immune system has been introduced. They can be developed by transplantation of (i) total peripheral blood from human healthy donors or patients, (ii) transplantation of tumour-infiltrating lymphocytes into immunodeficient mice or (iii) the transplant of CD34-positive human hematopoietic stem cells or precursors, either alone or in combination with additional human immune tissues into immunodeficient mice429,438. However, they are not in common use as they are highly expensive. Therefore, until the use of humanized PDX models becomes cost-effective, we propose MGS-A-x as a model of a multi-agent chemotherapy-refractory and crizotinib-resistant paediatric ALK+ ALCL to examine responses to novel therapies and the development of therapeutic resistance. We established the PDX via subcutaneous injection of MCs isolated from a bone marrow sample of a patient after CNS relapse and this sub-group of patients comprises the most difficult to treat cases with the 3-year OS for patients after CNS relapse being 48.7%100. Therefore, as a next step in the validation process of the PDX model, it will be important to test whether metastasis can be detected in the brain of the MGS-A-x mice. Subcutaneous PDX models are known to rarely metastasize439. Hence, IV injections could be attempted in the future. In vivo investigation of MGS-A-x showed that second generation ALK inhibitor brigatinib led to a reduction in the mean tumour volume relative to crizotinib treatment. In future work, it will be interesting to investigate the genetic and/or transcriptomic reasons why MGS-A-x shows resistance to crizotinib, but sensitivity to brigatinib. Most likely, MGS-A-x harbours an ALK mutations, which renders ALK+ ALCL tumour cells resistant to crizotinib, but not brigatinib. Based on published ALK-dependent resistance mechanisms in ALK+ NSCLC, L1152P/R214, C1156Y214, F1174V/C/L214,218, G1123S, F1127L or 1151Tins214,210,223 are possible ALK mutations likely to be detected. Hence, the FDA-approved second generation ALK inhibitor brigatinib could represent a treatment option for crizotinib-resistant ALK+ ALCL patients harbouring this ALK mutation. 113 CHAPTER 6 Overexpression of PIM1 in ALK+ malignancies decreases sensitivity to brigatinib and ceritinib 114 6.1 Introduction Deriving from precursor cells of the sympathetic nervous system, NB is the most common and deadly extracranial solid tumour in children440,441. NB presents at various sites along the sympathoadrenal axis, most commonly in the adrenal medulla or paraspinal ganglia442. Characterized by heterogeneous biological and clinical features ranging from spontaneous regression to aggressive treatment-resistant disease, NB is often referred to as a ‘clinical enigma’. While low- and intermediate-risk forms of NB are highly curable, over half of patients with high-risk disease suffer relapse and five-year survival is 40– 50%443. Therefore, novel treatment strategies aimed at providing long-term disease remission are urgently sought. ALK is the most commonly mutated gene in NB, where gain-of-function mutations in the kinase domain are found in 8-10% of cases overall177,444 (Figure 4B). An additional 2-3% of patients harbor focal amplification of ALK, and this feature correlates with poor survival177,444,445. Given the plethora of interest in the development of ALK inhibitors in NSCLC, the assessment of these compounds in ALK-driven NB quickly followed (Table 7). Numerous recent studies have demonstrated the efficacy of ALK inhibitors against ALK-driven NB cell lines and PDXs446–448. Several of these studies have documented the de novo resistance of the ALKF1174L mutation to crizotinib and ceritinib, and have devised combinatorial treatment strategies to enhance efficacy221,446,449–451. In patients with ALK+ NSCLC, acquired resistance has been shown to arise with first, second and third- generation ALK inhibitors, presenting a major challenge in the long-term use of these compounds452. The most common mechanisms of resistance to ALK inhibition in NSCLC are reported to involve bypass signaling through functionally-related pathways385. To identify mechanisms of resistance to ALK inhibitors in ALK-driven NB that involve bypass signaling, Liam C. Lee and Ricky M. Trigg conducted genome-wide CRISPR overexpression screens271 in the NB cell lines SH-SY5Y (ALKF1174L) and CHLA-20 (ALKR1275Q) under treatment with brigatinib or ceritinib for 14 days (Figure 8E). They identified putative resistance genes, of which the serine/threonine-protein kinase PIM1 was chosen for further investigation. PIM1 is a stress-response kinase with two isoforms being produced from alternative start codons453. PIM1 expression is normally regulated by a wide variety of cytokines, including those involved in JAK- STAT and NF-kB pathways454–456. Several oncogenic pathways have been identified as PIM1 targets457 including three pathways that facilitate inhibition of apoptosis457. Specifically, PIM1 phosphorylates the pro-apoptotic protein BCL2 associated agonist of cell death (BAD), thereby decreasing its interaction with the anti-apoptotic proteins B-cell lymphoma 2 (BCL2) and B-cell lymphoma extra-large (BCL-XL)458. In addition, PIM1 phosphorylates apoptosis signal-regulating kinase 1 (ASK1), which reduces its kinase activity. Inactivation of ASK1 leads to reduced phosphorylation of Jun N-terminal kinase (JNK) and p38, which in turn results in reduced caspase 3 activation459. Finally, PIM1 phosphorylates proline-rich Akt substrate of 40 kDa (PRAS40) inducing dissociation of PRAS40 from the mTOR complex (mTORC), thereby upregulating mTOR and unbound PRAS40 activity460,461. PIM1 expression has been associated with resistance to chemotherapy462–464 as well as molecularly targeted agents465,466, but not previously for NB. Hence, we further investigated if overexpression of PIM1 modifies sensitivity to ALK inhibition in ALK-related malignancies including NB and ALCL. 115 The data presented in this chapter of the thesis form sections of a publication in Nature Communications (Trigg, Lee & Prokoph et al.)300, which can be found in Appendix 1. 6.1.1 Aims This chapter aims to: • Perform individual validation assays for each of the 25 candidate genes to confirm their capability to induce resistance to ceritinib and brigatinib in SH-SY5Y/CHLA-20 cells • Determine whether PIM1 expression is predictive of OS in NB patients • Determine the clinical utility of pharmacologic inhibition of PIM1 alone or in combination with ALK inhibitors • Validate on-target effects of the CRISPR-based overexpression tool by reversing the resistant phenotype with RNAi against PIM1 • Determine the effect of PIM1 overexpression on sensitivity of ALK+ ALCL cell lines to brigatinib and ceritinib 6.2 Validation of candidate resistance genes in ALK-driven NB cells exposed to ALK inhibitors identified in a genome-wide CRISPR-Cas9 overexpression screen A genome wide sgRNA library containing 70,290 sgRNAs targeting 23,430 protein-coding genes271 was used for an overexpression screen conducted by Liam C. Lee and Ricky M. Trigg. SH-SY5Y/CHLA-20 cells were transduced with the sgRNA library, selected in zeocin for 7 days (day 0) and then cultured for 14 days (day 14) with brigatinib, ceritinib or DMSO, maintaining > 500 cells per sgRNA. Genomic DNA was extracted from cells at days 0 and 14, and deep sequencing conducted to identify enriched sgRNAs (Figure 8E). The read counts of two biological replicates were normalized for each sgRNA and candidate resistance genes were defined as those targeted by at least two sgRNAs showing >1.5-fold enrichment in ALK inhibitor-treated cells relative to DMSO treated cells300. Afterwards, all 25 candidates identified from the genome-wide CRISPR-Cas9 overexpression screens were functionally validated by transducing SH-SY5Y/CHLA-20 cells with two enriched sgRNAs individually and by assessing their response to brigatinib or ceritinib (Figure 34). First, levels of gene overexpression were assessed for all candidate genes by RT-qPCR (Figure 34A,D). Of the sgRNAs targeting 25 different genes, 76% (38/50) induced a significant increase in the ED50 concentration for both brigatinib and ceritinib in SH-SY5Y (p < 0.05) (Figure 34B-C), and 24 genes were validated. Similar data were obtained for the CHLA-20 cell line (Figure 34E-F), while MET was the top-ranking resistance gene. Five druggable genes were identified that may be amenable to either direct targeting (PIM1, PIK3CD and MET) or indirect targeting (KRAS and MYC). Given substantial evidence in the literature that PIM1 mediates resistance to standard chemotherapy462–464 as well as molecularly targeted agents465,466 and that high expression of PIM1 is a poor prognostic indicator in multiple cancers467–469, this gene was explored further. 116 Figure 34 Validation of CRISPR dCas9 overexpression screen hits in SH-SY5Y and CHLA-20 cells (A, D) RT-qPCR based gene expression in SH-SY5Y/CHLA-20 cells transduced with sgRNAs targeting candidate resistance inducing genes. Data were normalized to cells treated with NT sgRNA. Data represent mean ± SEM of technical triplicates. (B, E) Log10 transformed ED50 values from 96-hour dose response curves of (B) SH-SY5Y or (E) CHLA-20 cells treated with brigatinib or ceritinib five days post transduction with sgRNA molecules targeting each indicated candidate gene, ns = not significant, *p< 0 05, **p< 0 001, ***p< 0 0001 (one-way ANOVA). Data points represent mean SD of triplicates. (C, F) Representative 96-hour dose response curves of (C) SH-SY5Y or (F) CHLA-20 cells transduced with MET or non targeting sgRNAs. Data were analyzed for significance by one-way ANOVA. Data represent means +/- SEM of technical triplicates. Reproduced from Trigg, Lee & Prokoph et al.300. P IM 1 B D N F C O PZ 2 C Y B L E G R 4 E M L2 E TV 1 FA IM 2 FO XP 1 K R A S M ET M FS D 2A M YC N IN N K X 2- 4 N P Y P IK 3C D P R R X 2 R R A S S A G E 1 S E M A 4A S LC 7A 3 S S B P 3 S U R F2 U TF 1 no n- ta rg et in g 2.0 2.5 3.0 sgRNA 1 sgRNA 2 sgRNA-targeted candidate genes, brigatinib L o g 1 0 E D 5 0 ( n M ) ** * ** * ** * n s ** * * n s n s ** * ** * ** * ** * ** * * ** * n s ** * ** * ** * ** * ** * ** * ** * ** * ** * ** n s n s ** * ** * ** * ** * n s * ** * ** * ** * ** * ** * ** ** * ** * n s ** * ** * n s ** * ** * n s ** * P IM 1 B D N F C O PZ 2 C Y B L E G R 4 E M L2 E TV 1 FA IM 2 FO XP 1 K R A S M ET M FS D 2A M YC N IN N K X 2- 4 N P Y P IK 3C D P R R X 2 R R A S S A G E 1 S E M A 4A S LC 7A 3 S S B P 3 S U R F2 U TF 1 no n- ta rg et in g 2.0 2.5 3.0 sgRNA 1 sgRNA 2 sgRNA-targeted candidate genes, ceritinib L o g 10 E D 50 ( n M ) n s ** * ** * ** * ** * ** * n s n s ** * n s ** * ** * ** * ** * ** * n s ** * * ** * ** * ** * ** * ** * ** * ** * ** n s * * n s * ** * ** * ** * ** * ** * n s n s n s n s ** * ** * n s n s n s * ** * ** ** * * P IM 1 B D N F C O PZ 2 C LY B L E G R 4 E M L2 E TV 1 FA IM 2 FO XP 1 K R A S M ET M FS D 2A M YC N IN N K X 2- 4 N P Y P IK 3C D P R R X 2 R R A S S A G E 1 S E M A 4A S LC 7A 3 S S B P 3 S U R F2 U TF 1 0 2 4 6 8 10 100 200 300 20000 40000 60000 80000 100000 sgRNA 1 F o ld -c h a n g e g e n e e x p re s s io n (r e la ti v e t o n o n -t a rg e ti n g s g R N A ) sgRNA 2 A B 0 1 2 3 0 50 100 p < 0.0001 MET sgRNA NT sgRNA Log10 [ceritinib], (nM) C e ll vi a b ili ty ( % ) 0 1 2 3 0 50 100 p < 0.0001 MET sgRNA NT sgRNA Log10 [brigatinib], (nM) C e ll vi a b ili ty ( % ) C S H -S Y 5 Y P IM 1 B D N F C O PZ 2 C LY B L E G R 4 E M L2 E TV 1 FA IM 2 FO XP 1 K R A S M ET M FS D 2A M YC N IN N K X 2- 4 N P Y P IK 3C D P R R X 2 R R A S S A G E 1 S E M A 4A S LC 7A 3 S S B P 3 S U R F2 U TF 1 no n- ta rg et in g 0 1 2 3 4 sgRNA 1 sgRNA 2 sgRNA-targeted candidate genes, brigatinib L o g 1 0 E D 5 0 ( n M ) * ** ** * ** * n s n s n s * ** * ** * ** * n s ** * ** * ** * ** * ** * ** * * ** ** *** * n sn s ** * ** * n s *** * ** * * ** ** * ** *** * n s ** * n s ** * n s ** * ** * ** * ** * ** * n s ** * ** * n s n s n s n s n s D P IM 1 B D N F C O PZ 2 C LY B L E G R 4 E M L2 E TV 1 FA IM 2 FO XP 1 K R A S M ET M FS D 2A M YC N IN N K X 2- 4 N P Y P IK 3C D P R R X 2 R R A S S A G E 1 S E M A 4A S LC 7A 3 S S B P 3 S U R F2 U TF 1 0 2 4 6 8 10 12 200 400 600 800 1000 100000 200000 300000 400000 500000 sgRNA 1 F o ld -c h a n g e g e n e e x p re s s io n (r e la ti v e t o n o n -t a rg e ti n g s g R N A ) sgRNA 2 E P IM 1 B D N F C O PZ 2 C Y B L E G R 4 E M L2 E TV 1 FA IM 2 FO XP 1 K R A S M ET M FS D 2A M YC N IN N K X 2- 4 N P Y P IK 3C D P R R X 2 R R A S S A G E 1 S E M A 4A S LC 7A 3 S S B P 3 S U R F2 U TF 1 no n- ta rg et in g 0 1 2 3 4 5 sgRNA 1 sgRNA 2 sgRNA-targeted candidate genes, ceritinib L o g 10 E D 50 ( n M ) n s * ** ** * ** * ** * n s n s ** * n s ** ** ** ** * ** * ** * ** * * ** ** * ** * ** * ** * * ** ** * n s * n s ** * ** * ** * **** * n sn s n s n s ** * n s n s n s * * n s n s ** * n s n s n s ** * n s n s 0 1 2 3 0 50 100 Log10 [ceritinib], (nM) C e ll vi a b ili ty ( % ) NT sgRNA MET sgRNA p < 0.0001 0 1 2 3 0 50 100 Log10 [brigatinib], (nM) C e ll vi a b ili ty ( % ) NT sgRNA MET sgRNA p < 0.0001 F C H L A -2 0 117 6.3 PIM1 inhibition enhances the sensitivity of high-risk aberrant ALK- expressing NB to ALK inhibition regardless of MYCN status 6.3.1 High expression of PIM1 in NB is associated with advanced, high risk disease independent of MYCN amplification Recently, Brunen et al.470 identified PIM kinases as potential therapeutic targets in NF1 wild-type NB and demonstrated that high PIM expression is associated with poorer OS in NB patients. In support of PIM1 as a prognostic biomarker, we found its high expression to be significantly associated with worse OS in an independent cohort of NB patients (n = 498) (Figure 35A)364. Interestingly, PIM1 transcript level serves as a prognostic biomarker independent of MYCN status in this cohort (Figure 35B-D). However, as reported by Brunen et al.470, we also found MYCN amplification to be a stronger predictor of poor prognosis than PIM1 (Figure 35A-B). Figure 35 High expression of PIM1 in NB is associated with advanced, high risk disease independent of MYCN amplification (A-D) NB patients (n = 498) were divided into two groups according to (A) PIM 1 expression, (B) MYCN amplification status, (C) PIM1 expression in MYCN- patients or (D) PIM1 expression in MYCN+ patients and the difference in median OS (log-rank test, Bonferroni corrected) was analyzed using the Kaplan–Meier estimator. Reproduced from Trigg, Lee & Prokoph et al.300. 6.3.2 Inhibition of PIM1 alone lacks potency in ALK-expressing NB but enhances the efficacy of ALK inhibitors The activity of the pan-PIM inhibitor AZD1208471 (with greatest potency for PIM1) was then determined in ALK-driven NB cell lines by 72-hour dose-response assays (Figure 36A-B). Consistent with data reported by Brunen et al.470, cell lines were relatively insensitive to AZD1208471 at clinically-relevant concentrations, with predicted ED50 values exceeding 10 µM in 8/8 NB cell lines expressing a range of ALK mutants (Figure 36A). Similar results were noted in response to treatment with IBL-PIMi, another small-molecule pan-PIM kinase inhibitor in preclinical development (Figure 36B), suggesting that pharmacological inhibition of PIM kinases alone is not a viable therapeutic strategy. The response of ALK- NB cell lines to AZD1208 and IBL-PIMi was likewise analyzed and a similar response was observed, indicating that the response to PIM inhibitors is independent of ALK status (Figure 36C-D). 0 50 100 150 0 20 40 60 80 100 Follow up (months) O v e ra ll s u rv iv a l (% ) low (n = 70) high (n = 22) p = 0.15 MYCN+ 0 50 100 150 200 250 0 20 40 60 80 100 Follow up (months) O v e ra ll s u rv iv a l (% ) low (n = 300) high (n = 101) p = 0.0018 MYCN- BA 0 50 100 150 200 250 0 20 40 60 80 100 Follow up (months) O v e ra ll s u rv iv a l (% ) low (n = 383) high (n = 115) p = 0.00062 0 50 100 150 200 250 0 20 40 60 80 100 Follow up (months) O v e ra ll s u rv iv a l (% ) MYCN- MYCN+ p < 0.0001 DC PIM1 PIM1 PIM1 PIM1 PIM1 PIM1 118 Figure 36 Response of ALK+ and ALK- NB cell lines to PIM inhibition (A, B) 72-hour dose-response assays for (A) ALK+ or (B) ALK- NB cell lines treated with AZD 1208 or IBL-PIMi. Data represent means +/- SEM of technical triplicates. Reproduced from Trigg, Lee & Prokoph et al.300. Recent human dose-escalation studies have displayed general tolerability for the PIM inhibitor AZD1208471, which prompted the assessment of combined ALK and PIM1 inhibition in our study. To this end, cell viability following 72 hours exposure to AZD1208 in combination with brigatinib or ceritinib in KELLY (MYCN-amplified) cells using dose-response matrices in a log-scale format was analysed. The drug interactions were characterized using the Bliss Independence model362. A wide range of Bliss combination index (CI) values were determined across the concentration ranges for both ALK inhibitors, but for the most part CI values were <1, indicative of mild synergy between ALK and PIM inhibition (Figure 37). Figure 37 ALK inhibitors and AZD1208 exhibit mild synergism in KELLY cell lines Heat maps representing the viability of KELLY cell lines based on normalized CellTiter-Blue fuorescent reads on exposure to log scale (0, 1, 3, 10, 30, 100, 300, 1000, 3000 nM) concentrations of AZD1208 and ALK inhibitors (brigatinib, ceritinib) for 72 hours. Drug doses used span upon and below the publicly available EC50 values of the individual drugs used446. Numbers indicate the Bliss combination index (CI) values for each dose pair. The Bliss independence model362 was used to calculate CI values. CI= (Ea+Eb-((Ea*Eb))/Eab, where Ea indicates the viability effect of drug A (ALK inhibitor), Eb indicates the viability effect of drug B (AZD1208) and Eab indicates the viability effect of the drug combination. CI < 1 indicates synergism, CI = 1 indicates additivity and CI > 1 indicates antagonism. Synergistic dose combinations (threshold ≤ 0 95) are shown in italic. Data points are representative of two independent experiments. Reproduced from Trigg, Lee & Prokoph et al.300. B A ALK+ 0 1 2 3 4 50 60 70 80 90 100 110 Log10 [AZD1208] (nM) C e ll vi a b ili ty ( % ) SH-SY5Y CHLA-90 CHLA-20 COG-N-426 KELLY LAN-5 CHLA-95 NB-1643 0 1 2 3 4 50 60 70 80 90 100 110 Log10 [IBL-PIMi] (nM) C e ll vi a b ili ty ( % ) CHLA-90 COG-N-426 CHLA-20 KELLY LAN-5 CHLA-95 SH-SY5Y NB-1643 0 1 2 3 4 50 60 70 80 90 100 110 Log10 [AZD1208] (nM) C e ll vi a b ili ty ( % ) CHLA-171 GIMEN LAN-6 NBL-S NGP 0 1 2 3 4 50 60 70 80 90 100 110 Log10 [IBL-PIMi] (nM) C e ll vi a b ili ty ( % ) CHLA-171 GIMEN LAN-6 NBL-S NGP ALK- 1000 - 0.94 0.89 0.94 0.99 1.02 1.01 0.99 600 - 1.00 0.95 0.97 1.00 1.03 1.01 0.99 300 - 1.00 0.93 0.96 1.00 1.04 1.01 0.99 100 - 0.99 1.04 1.01 1.07 1.06 1.01 0.98 60 - 1.22 0.96 0.96 1.03 1.04 1.02 0.98 30 - 1.21 0.91 1.05 1.02 1.03 1.02 1.00 10 - 0.80 0.83 1.00 0.95 1.01 1.00 1.00 0 - - - - - - - - 0 10 30 60 100 300 600 1000 1000 - 0.91 0.92 0.93 0.98 1.01 1.00 0.99 600 - 0.92 0.92 0.95 1.00 1.02 1.01 0.99 300 - 0.89 0.89 0.92 0.97 1.02 1.00 0.99 100 - 1.19 0.95 0.92 0.97 1.06 1.02 0.98 60 - 1.29 1.18 1.05 1.14 1.15 1.12 1.00 30 - 1.02 1.18 1.03 1.06 1.08 1.02 1.00 10 - 0.87 0.94 0.91 0.99 1.04 0.99 0.97 0 - - - - - - - - 0 10 30 60 100 300 600 1000 A Z D 1 2 0 8 ( n M ) Brigatinib (nM) A Z D 1 2 0 8 ( n M ) Ceritinib (nM) Cell viability 100% 0% 119 6.3.3 Knockdown of PIM1 sensitizes NB cells to ALK inhibitors As AZD1208 is a pan-PIM kinase inhibitor, KELLY (MYCN-amplified) cells were transduced with a PIM1- targeting shRNA to confirm the specificity of the potentiation effects described above. We achieved an approximate 50% reduction in PIM1 expression as confirmed by RT-qPCR (Figure 38A). PIM1 knockdown increased the sensitivity of cells to brigatinib and ceritinib, indicated by a significant decrease in ED50 concentrations after 72 hours of treatment (p < 0.0005) (Figure 38B). Figure 38 Knockdown of PIM1 sensitizes NB cells to ALK inhibitors (A) Analysis of PIM1 levels by RT-qPCR in KELLY cells with shRNA-mediated knockdown of PIM1. NT=non- targeting. RT-qPCR data represents the means + SD of triplicate experiments. (B) 72-hour dose-response assays following brigatinib or ceritinib exposure in KELLY cells treated with PIM1-targeting and non-targeting (NT) shRNA. Data points represent the mean of triplicate experiments. ED50 values were compared by an unpaired Student’s t- test. Reproduced from Trigg, Lee & Prokoph et al.300. 6.4 Overexpression of PIM1 in ALK+ ALCL cell lines decreases sensitivity to ALK inhibitors Given that a strong synergistic effect was previously shown on simultaneous inhibition of ALK and PIM kinases in ALK+ ALCL cell lines472, overexpression of PIM1 was induced and sensitivity to ALK inhibitors monitored in ALK+ ALCL cell lines. K299 and SU-DHL-1 cells were transduced to express the dCas9- VP64/MS2-P65-HSF1 components whose activity was confirmed (Figure 11) before assessing their responses to brigatinib or ceritinib upon overexpression of PIM1. Indeed, overexpression of PIM1 led to drug resistance, evidenced by significant increases in the brigatinib ED50 (p < 0.01) and ceritinib ED50 (p < 0.001) (Figure 39). Therefore, PIM1 is a potential ALK inhibitor-resistance driver in ALCL and is worthy of further exploration. BA 0 1 2 3 4 0 50 100 NT shRNA PIM1 shRNA IC50 Non-targeting shRNA 297.1 PIM1 shRNA 81.55 p < 0.0001 Log10 [ceritinib], (nM) C e ll vi a b ili ty ( % ) N T sh R N A P IM 1 sh R N A 1 P IM 1 sh R N A 2 0.0 0.5 1.0 F o ld -c h a n g e P IM 1 e x p re s s io n CA 0 1 2 3 4 0 20 40 60 80 100 Log10 [brigatinib], (nM) C e ll vi a b ili ty ( % ) PIM1 shRNA 1 NT shRNA PIM1 shRNA 2 *** **p = 0.0004 **p < 0.0001 0 1 2 3 4 0 20 40 60 80 100 Log10 [ceritinib], (nM) C e ll vi a b ili ty ( % ) PIM1 shRNA 1 NT shRNA PIM1 shRNA 2 p < 0.0001 N T sh R N A P IM 1 sh R N A 1 0.0 0.5 1.0 F o ld -c h a n g e P IM 1 e x p re s s io n 0 1 2 3 4 0 50 100 NT shRNA PIM1 shRNA IC50 Non-targeting shRNA 295.8 PIM1 shRNA 99. 1 p < 0.0005 Log10 [brigatinib], (nM) C e ll v ia b ili ty ( % ) CHLA-20 KELLY DB 120 Figure 39 Overexpression of PIM1 in ALK+ ALCL cell lines decreases sensitivity to ALK inhibitors (A) Expression of PIM1 in K299 and SU-DHL-1 cells 5 days post-transduction with PIM1-targeted sgRNA relative to NT sgRNA, as determined by RT-qPCR. Data points represent the mean +/- SEM of triplicate experiments. (B) 48-hour dose-response assay of brigatinib or ceritinib in K299 and SU-DHL-1 cells treated with PIM1-targeting and NTsgRNA. Data points represent the mean +/- SEM of triplicate experiments. ED50 values were compared by unpaired Student’s t-test. Reproduced from Trigg, Lee & Prokoph et al.300. 6.5 Discussion This work has expanded on the findings of previous publications investigating ALK inhibitor resistance mechanisms in NB446,473,474; AXL activation was identified by a phospho-proteomic assay in NB cell lines rendered resistant to ALK inhibitors through continuous exposure to increasing concentrations of drugs473, whereas MYCN overexpression was noted as a resistance mechanism in another study446. Of note, neither MYCN nor AXL were among the resistance driver candidates identified. This may be due to insufficient overexpression of MYCN or AXL induced by the CRISPR-dCas9-VP64 platform as variable overexpression levels were observed for different genes. Ultimately, the in vitro studies conducted to date are potentially predictive of resistance mechanisms in patients but until more children with NB have been treated with ALK inhibitors and biopsy material is taken for study at relapse, CRISPR screens, whilst having their caveats offer the best approach for global, unbiased screening for resistance mechanisms. Among the validated genes mediating sensitivity to ALK inhibition in the two tested NB cells were genes known to mediate resistance in other ALK+ malignancies. Activation of MET has previously been shown to confer resistance to alectinib in ALK+ NSCLC475. Two are known to be activated downstream of ALK, namely KRAS and PIK3CD. Copy number gain and mutational activation of KRAS at codon 12 has been shown to confer resistance to crizotinib and ceritinib in ALK+ NSCLC234,370. Similarly, mutational activation of PIK3CA is reported as a resistance mechanism to alectinib and ceritinib in these patients216,243. Interestingly, we identified a resistance gene (MFSD2A)476 encoding a sodium-dependent transporter of fatty acids expressed in brain endothelium, in both SH- SY5Y and CHLA-20 cells treated with brigatinib or ceritinib, most likely functioning as an efflux pump for ALK inhibitors although this remains to be investigated further. BA K 29 9 S U -D H L- 1 0 2 4 6 8 F o ld -i n c re a s e P IM 1 e x p re s s io n 0 1 2 3 4 0 50 100 Log10 [brigatinib], (nM) C e ll vi a b ili ty ( % ) NT sgRNA PIM1 sgRNA p < 0.01 0 1 2 3 4 0 50 100 Log10 [brigatinib], (nM) C e ll vi a b ili ty ( % ) PIM1 gRNA NT sgRNA p < 0.0001 0 1 2 3 4 0 50 100 Log10 [ceritinib], (nM) C e ll vi a b ili ty ( % ) NT sgRNA PIM1 gRNA p < 0.001 0 1 2 3 4 0 50 100 Log10 [ceritinib], (nM) C e ll v ia b ili ty ( % ) NT sgRNA PIM1 sgRNA p < 0.0001 K299 SU-DHL-1 121 MET was the only putative resistant gene common to all ALK inhibitors. Given that crizotinib is a potent inhibitor of MET475 and can overcome ALK inhibitor resistance driven by activation of MET in NSCLC477, we chose to focus instead on PIM1 whereby high PIM1 gene expression levels were found to be associated with advanced, high-risk disease and poor survival outcomes on analysis of published datasets363,364. In addition to validating PIM1 as a resistance gene in NB cell lines, we sought to determine whether PIM1 induces resistance to ALK inhibition in another ALK-driven paediatric cancer, namely ALCL. Of the transgenic mice expressing PIM1 and MYC under control of the immunoglobulin heavy chain enhancer that were cross-bred, the double transgenic mice developed T-lymphomas around birth. This established the oncogenic nature of PIM1 and its cooperation with MYC in the formation of lymphoid tumours478. Indeed, overexpression of PIM1 in ALK+ ALCL cell lines decreased sensitivity to brigatinib and ceritinib, consistent with results published previously demonstrating robust synergy between a small-molecule pan-PIM inhibitor and crizotinib in ALCL cell lines472. Therefore, further studies investigating the potential for combined PIM and ALK inhibition in other ALK+ malignancies are warranted. We assessed the in vitro responses of both ALK-driven and ALK-negative NB cells to several small- molecule pan-PIM kinase inhibitors and found that cells were relatively insensitive after 72 hours of exposure. However, knockdown of PIM1 by RNAi sensitized cells to ALK inhibition and the combination of ALK inhibitors with AZD1208 demonstrated mild synergy. Therefore, our data suggest that PIM1 induces resistance to ALK inhibitors in NB cell lines and demonstrate the potential for combined pharmacological inhibition of ALK and PIM1 in patients with ALK-driven, high-risk NB. However, the mechanism of how overexpression of PIM1 modifies sensitivity to ALK inhibition remains to be determined. Published studies on PIM1 overexpression in other cancer types provide possible suggestions. Studies of hematological malignancies479, gastric cancer480 and head and neck cancer481 have demonstrated that aberrant expression of PIM1 was associated with poor survival. PIM1 overexpression associated with LN metastasis, histology and poor clinical outcome in both lung adenocarcinoma and squamous cell carcinoma482, while it appeared to be a favorable prognostic factor in pancreatic cancer483. Furthermore, PIM1 has emerged as a driver of drug resistance in various cancer types478,484 including T-cell lymphomas (TCLs) and EBV positive lymphomas 485,486, DLBCL466, acute myeloid leukemia (AML)463,487, breast cancer488,489, prostate cancer490 and ovarian cancer491. More specifically, three studies on lung adenocarcinoma492, prostate cancer490 and adult T-cell leukemia (ATL)493 found that PIM1 is a central mediator of STAT3 signaling, two studies on chronic lymphocytic leukaemia (CLL)494 and myeloproliferative neoplasms495 found that PIM1 triggered mTOR pathway activity, three studies on lung adenocarcinoma492, breast cancer489 and prostate cancer496 found that PIM1 potentiated PI3K/AKT signaling and one study on lung adenocarcinoma492 found that PIM1 potentiated RAS/ERK signaling. Furthermore, PIM1 overexpression in TCLs485 and ovarian cancer497 lead to upregulation of MYC. In addition, PIM1 affected NF-κB signaling in the ABC subtype of DLBCL466 and in prostate cancer490, while phosphorylation of MET was observed in lung adenocarcinoma492 and prostate cancer496. Future investigations will tell which pathways are involved in mediating ALK inhibitor resistance in ALK-driven, high-risk NB. 122 CHAPTER 7 Detection and clinical significance of anti-ALK autoantibodies 123 7.1 Introduction 7.1.1 Humoral Immune Response against ALK in ALK+ ALCL While ALK is highly expressed in the nervous system during embryogenesis3,498, it is almost absent in developed tissues except in a few neurons within the CNS and in the spinal cord499. Aberrant expression of tumour antigens that are not expressed in healthy tissue can induce the production of autoantibodies500. Therfore, Karen Pulford and colleagues investigated whether a humoral immune response against ALK in ALK+ ALCL patients exists88. An immunoperoxidase labelling technique for NPM1-ALK transfectants (section 2.4) was used as a detection method. All analyzed (100%; 11/11 patients) ALK+ ALCL patients, but not healthy controls (n=5), had detectable anti-ALK antibodies specific for the oncoantigen88. In a subsequent analysis by the same group, the presence of anti-ALK autoantibodies in ALK+ ALCL patients was further confirmed at different time points after diagnosis89. These results were confirmed by an independent group, which detected anti-ALK autoantibodies in ALK+ ALCL patients (>80%, 25/28 patients)90. Importantly, patients who presented with higher anti-ALK antibodies antibody levels prior to and after chemotherapy treatment, had a trend toward a reduced relapse risk90. These results raised the possibility that the favorable prognosis of ALK+ ALCL could be contributed to the activation of the immune system88. This spurred an investigation into the clinical significance of anti-ALK autoantibody titres in a larger patient cohort. In a combined effort, Woessmann and Pulford analyzed anti-ALK autoantibodies in 95 paediatric ALK+ ALCL patients that were recruited onto comparable short-pulse chemotherapy trials (NHL-BFM9033/NHL-BFM9594) prior to treatment initiation. They confirmed that >90% of the ALK+ ALCL patients had measurable anti-ALK autoantibody titers at diagnosis compared to 1/99 controls91. They further categorized patients into low (≤1/750), intermediate (1/750 to <1/60,750) and high (≥1/60,750) anti-ALK autoantibody titre groups. Interestingly, the magnitude of the antibody response inversely correlated with relapse risk91. The cumulative incidence of relapse was 11 ± 6%/31 ± 8%/63 ± 10% for patients in the high/intermediate/low titre group91. Next, Woessmann and colleagues combined anti-ALK autoantibody titre and MDD39. This way ALK+ ALCL patients could be stratified into three biological risk groups (bRG): high risk (bHR): MDD-positive and antibody titre ≤ 1/750, low risk (bLR): MDD negative and antibody titre >1/750, intermediate risk (bIR): all remaining patients. PFS was 28%, 68% and 93% for bHR, bIR and bLR, respectively. Five year OS was 71%, 83% and 98% for bHR, bIR and bLR39. Finally, a systematic analysis of the course of anti-ALK autoantibody titres during treatment was performed in 122 paediatric ALK+ ALCL patients that were recruited onto comparable short-pulse chemotherapy studies (NHL-BFM9594/Associazione Italiana di Ematologia e Oncologia Pediatrica (AIEOP) LNH‐9795/ALCL9928/NHL‐BFM 2012 registry)92. The EFS of paediatric ALK+ ALCL patients with anti-ALK autoantibody titres of >1/750 at the end of therapy was 93 ± 5% compared to 65 ± 5% for patients with anti-ALK autoantibody titres below this cut‐off92. They further categorized paediatric ALK+ ALCL patients according to the decrease in the anti-ALK autoantibody titre from diagnosis to the end of therapy: patients with very low initial titres (≤1/250), patients who showed a titre‐decrease of maximal 124 two dilution steps (≤2) or patients who showed a titre‐decrease of more than two dilution steps (>2). 10- year EFS was 52 ± 9%, 91 ± 5% or 70 ± 6% for patients in the ≤1/250, ≤2 or >2 group92. 7.1.2 Humoral Immune Response against ALK in ALK+ NSCLC As ALK rearrangement and ALK upregulation have been described in other cancers, ALK might serve as an embryonal tumour-associated antigen in other ALK+ malignancies. The first indication was published by Chiarle and colleagues who demonstrated that ALK vaccination is not only effective in preventing disease relapse in a murine tumour model of NPM1-ALK+ ALCL, but also in a murine tumour model of EML4-ALK+ NSCLC142,501. One year later, a pilot study showed the presence of ALK autoantibodies in the sera of 13/21 ALK+ NSCLC patients502. This observation was confirmed in an independent study that detected anti-ALK autoantibodies in 9/53 ALK+ NSCLC patients, but not in 0/38 ALK- NSCLC patients503. The first study502 utilized an immunoperoxidase labelling technique for NPM1- ALK transfectants (section 2.4) as the detection method, while the second study503 developed an enzyme-linked immunosorbent assay (ELISA) to measure anti-ALK autoantibody levels in a mixed population of treatment-naïve and ALK inhibitor/chemotherapy treated patients and detected anti-ALK autoantibodies in 62%502 and 17%503 of ALK+ NSCLC patients, respectively. Although highly variable, these data indicate that an ALK-specific immune response exists in a fraction of ALK+ NSCLC patients with possible prognostic application. Although statistically not significant, ALK+ NSCLC patients with higher anti-ALK autoantibody levels showed a trend towards more favorable OS outcomes503. However, whether the presence of anti-ALK autoantibodies in ALK+ NSCLC patients confers a more favorable prognosis warrants further investigation. 7.1.3 Aims This chapter aims to: • Develop a protein microarray assay to determine circulating anti-ALK autoantibody levels in serum, plasma, and frozen whole blood • Cross-validate the newly developed protein microarray with the old immunoperoxidase labelling technique for NPM1-ALK transfectants using samples from treatment-naïve paediatric ALK+ ALCL enrolled onto the ALCL-99 (NCT00006455) trial 125 7.2 A pipeline to quantify ALK autoantibody titres in ALK+ malignancies Circulating ALK antibody titres have so far been evaluated using an immunoperoxidase labelling technique for NPM1-ALK transfectants. However, the assay is labour-intensive, difficult to standardize amongst multiple labs and subjective in interpretation leading to false positive results. Therefore, in collaboration with Cambridge Life Sciences, a protein microarray assay was developed that can be fully automated (Figure 40). The printing of the teflon mask and the functionalization of the glass slides were outsourced. The antigen spotting took place at Cambridge Life Sciences. The analysis of microarray slides was optimized for both an automated slide processor commonly available in hospitals and for manual handling in research laboratories. Figure 40 Production, quality control (QC) and processing of microarray slides Glass slides are masked with Teflon, the surface is functionalized for antigen binding with a 2-D Epoxy surface and labelled. Antigens are spotted with a picolitre droplet dispenser. In-process controls monitor the angle of the droplet dispersion before and after each individually dispensed antigen (QC1), and the successful spotting with water- sensitive slides that turn from yellow to blue (QC2). After the droplets are dispensed, the even distribution of the antigens is confirmed by scanning for a fluorescent dye that was included in the antigen dilution buffer (QC3). Two slides out of each print run are processed (QC4) either with an automated slide processor (e.g. ZENIT UP) or handled manually and scanned for fluorescent intensity (e.g. with a Zenit AmiDot reader) before the final product is used to evaluate patient samples. water-sensitive slides Plane glass slide Glass slide with teflon mask Functionalized glass slide Functionalization with 2-D Epoxy surface barcode labelling Printing of antigens In-process control In-process control Scann for fluorescent dye included in antigen dilution buffer Monitoring of droplet dispersion with camera Automated slide processing Scanning Printing of teflon mask Outsourced Final Product QC2 QC3 QC1 QC1-3 passedManual handling QC4 126 7.2.1 2D-Epoxy is the best slide activation chemistry for antigen binding As a first step in the optimization process, 2-dimensional (2D)-Epoxy, 3-dimensional (3D)-Epoxy, 3D- N-Hydroxy succinimide (NHS), 2D-Aldehyde and 3D-Aldehyde functionalized glass slides were evaluated for their ability to form covalent bonds with DyLight550 conjugated human IgG that was used as a detection antibody in the final assay set-up (Figure 41). 2D-Epoxy surfaces showed the best binding properties (Figure 41C) for coupling of biochemical species via nucleophilic groups such as amines, thiols and hydroxyl groups via formation of a covalent bond (Figure 41B). Additionally, Epoxy surfaces show better stability than NHS and Aldehyde surfaces as they are stable to temperatures of 40 °C and to humid conditions rendering them ideal for commercial production of slides. Unspecific binding to surfaces with printed PBS blank (data not shown) occurred for 3D sides. Therefore, 2D-Epoxy slides were chosen for further optimization. Figure 41 Effect of slide activation chemistry (A) A cartoon demonstrating the 2-dimensional (2D) and 3-dimensional (3D) functional matrices that were tested. (B) Visual representation of the Epoxy (violet, orange), NHS (blue) and Aldehyde (green, red) functional groups that were tested. (C) Increasing concentrations of DyLight550 conjugated Hu-IgG were printed on 2D-Epoxy, 3D- Epoxy, 3D-NHS, 2D-Aldehyde, and 3D-Aldehyde functionalized glass slides. Coupling properties were evaluated via the signal intensity of the printed antigen. 5 0 1 0 0 1 5 0 2 0 0 0 5 0 0 0 0 0 1 0 0 0 0 0 0 1 5 0 0 0 0 0 2 0 0 0 0 0 0 D y L ig h t5 5 0 c o n ju g a te d H u -Ig G (U /m L ) S ig n a l In te n s it y 2 D E p o x y 3 D N H S 3 D A ld e h y d e 3 D E p o x y 2 D A ld e h y d e 3-dimensional (3D) functional matrix 2-dimensional (2D) functional matrix Functional group Antifouling Matrix Spacer Glass A B C + H2N Biochemical Species Biochemical Species Biochemical Species Biochemical Species+ H2N + H2N NH NH NH poxy S ldehyde ldehyde y Biochemical Species Biochemical Species 127 7.2.2 A reduced teflon mask increases the signal intensity For evaluation of slide activation chemistry, glass slides without a mask (Figure 42A) were utilized. Next, a Teflon mask was introduced so that the slides could be processed with an automated slide processor. First, we tested a full mask. However, we observed a 3-fold decrease in signal intensity (Figure 42B) that might have occurred due to interference of the Teflon mask with the functionalization of the surface with 2D-Epoxy. Therefore, a reduced mask was designed leading to increased signal intensity as compared to the use of a full mask (Figure 42B). Figure 42 Effect of the Teflon mask (A) The illustrated Teflon masks needed for automated processing of glass slides were coated with a 2D-Epoxy surface and analysed for their assay performance. (B) Effect of the Teflon mask on the signal intensity of DyLight550 conjugated human IgG. 7.2.3 Evaluation of ALK and control proteins Damm-Welk et al. showed that sera or plasma from ALK+ NSCLC patients stained both EML4-ALK and NPM1-ALK transfectants, and sera or plasma from ALK+ ALCL patients reacted to NPM1-ALK, TPM3- ALK and full-length-ALK transfected COS cells502. In addition, epitopes within the intracytoplasmic domain of ALK recognized by ALK autoantibodies have been described in sera or plasma from nine ALK+ NSCLC503 and 129 ALK+ ALCL504 patients. Collectively, these results suggest that anti-ALK autoantibodies target the ALK-portion of the fusion proteins502. Hence, we used commercially available full-length ALK protein for the microarray. Full-length ALK protein is insoluble at concentrations utilized in the microarray assay (data not shown), therefore an ALK protein with an uncleaved GST-tag was purchased. Besides ALK-GST, several control proteins were chosen (Figure 43A). GST protein was used to detect unspecific binding of autoantibodies. Anti-human IgG was spotted as a positive control that binds all human IgG antibodies. Human IgG was utilized for use in standard curve measurements. DyLight550 conjugated IgG served as a recognition spot for the scanner during QC3 (Figure 40) before slides where processed and incubated with the detection antibody. 1 2 3 4 5 6 7 8 1 2 3 4 5 6 7 8 A B C D No mask Reduced mask Full mask 0 5 0 1 0 0 1 5 0 2 0 0 0 2 5 0 0 0 0 5 0 0 0 0 0 7 5 0 0 0 0 1 0 0 0 0 0 0 1 2 5 0 0 0 0 D y L ig h t5 5 0 c o n ju g a te d H u -Ig G (U /m L ) S ig n a l In te n s it y n o m a s k fu ll m a s k E 3 8 8 6 -0 1 9 re d u c e d m a s k E 3 9 0 3 -0 0 2 0 2 5 5 0 7 5 1 0 0 0 2 5 0 0 0 0 5 0 0 0 0 0 7 5 0 0 0 0 1 0 0 0 0 0 0 1 2 5 0 0 0 0 1 5 0 0 0 0 0 A n ti H u -Ig G C o n c (U /m L ) S ig n a l In te n s it y n m a s k fu ll m a s k E 3 8 8 6 -0 1 9 re d u c e d m a s k E 3 9 0 3 -0 0 2 0 2 5 5 0 7 5 1 0 0 0 2 5 0 0 0 0 5 0 0 0 0 0 7 5 0 0 0 0 1 0 0 0 0 0 0 1 2 5 0 0 0 0 1 5 0 0 0 0 0 H u -Ig G C o n c (U /m L ) S ig n a l In te n s it y n o m a s k fu ll m a s k E 3 8 8 6 -0 1 9 re d u c e d m a s k E 3 9 0 3 -0 0 2 N Fu l Reduc 128 To investigate the ability of the microarray to detect ALK autoantibodies in patient samples, we measured sera from two ALK+ ALCL patients (Figure 43B) that had previously tested positive using the immunoperoxidase labelling technique of NPM1-ALK transfectants. Unspecific binding was not observed for frozen whole blood or plasma from one healthy individual and serum from a pool of 100 healthy individuals. Therefore, as opposed to the immunoperoxidase labelling technique for NPM1-ALK transfectants, this assay enables the detection of ALK autoantibodies in not only serum and plasma but also frozen whole blood. Figure 43 Antigens utilized in the microarray assay (A) Autoantibodies specific for human ALK bind and are detected with DyLight 550 conjugated IgG. GST protein serves as a control for unspecific binding of circulating autoantibodies to the GST-tag of the ALK protein. Human IgG serves as a control to confirm activity of the secondary antibody and is utilized for a standard curve. Anti-human IgG detects whether saturating levels of patient samples are used. (B) Binding of circulating autoantibodies in donor samples to the indicated antigens on the microarray. ALK Fluoro phore Human IgG Anti-human IgG Anti-ALK autoantibody Fluoro phore IgG DyLight 550 conjugated IgG Fluoro phore A Fluoro phore GST B healthy plasma healthy serum healthy frozen whole blood ALK+ ALCL serum sample 1 ALK+ ALCL serum sample 2 ALK-GST human IgG anti-human IgG 129 7.2.4 Final slide layout A final slide layout with 8 wells (Figure 44) was chosen to enable a high throughput analysis of clinical trial samples. However, in the future the number of wells could be adjusted to reflect the low incidence of ALK+ ALCL patients. Each well is able to accommodate 81 antigen spots (Figure 44B-C) with 29 spots being occupied by control antigens leaving space for 17 different antigens in triplicates. Figure 44 A typical slide layout (A) Position of the barcode. (B) The pattern of 9x9 possible antigen spots per well. (C) The grid with antigen positions used in the final microarray indicated. 43896 ALK 8-well 2018-09-E3904 004 Cambridge Life Science Ltd. B 2 .5 c m B B B B B B B 7.5 cm A 0 .9 c m 1 2 3 4 5678 A B C 1 2 3 4 5 6 7 8 9 1 200 ug/mL DyLight conj 12.5 ug/mL Anti-Hu IgG 25 ug/mL Anti-Hu IgG 50 ug/mL Anti-Hu IgG 100 ug/mL Anti-Hu IgG 200 ug/mL Anti-Hu IgG 200 ug/mL DyLight conj 2 100 ug/mL Hu IgG 100 ug/mL Hu IgG 100 ug/mL Hu IgG 12.5 ug/mL Anti-Hu IgG 25 ug/mL Anti-Hu IgG 50 ug/mL Anti-Hu IgG 100 ug/mL Anti-Hu IgG 200 ug/mL Anti-Hu IgG 3 12.5 ug/mL Anti-Hu IgG 25 ug/mL Anti-Hu IgG 50 ug/mL Anti-Hu IgG 100 ug/mL Anti-Hu IgG 200 ug/mL Anti-Hu IgG 4 150 ug/mL ALK 150 ug/mL ALK 150 ug/mL ALK 200 ug/mL DyLight conj 5 75 ug/mL ALK 75 ug/mL ALK 75 ug/mL ALK Buffer blank 6 37.5 ug/mL ALK 37.5 ug/mL ALK 37.5 ug/mL ALK Buffer blank 7 18.75 ug/mL ALK 18.75 ug/mL ALK 18.75 ug/mL ALK Buffer blank 8 150 ug/mL GST 150 ug/mL GST 150 ug/mL GST 9 200 ug/mL DyLight conj 200 ug/mL DyLight conj C 130 7.3 Protein microarray assay cross-validation To cross-validate the microarray assay with the immunoperoxidase labelling technique we determined the presence of anti-ALK antibodies in plasma or serum samples of 93 paediatric ALK-positive ALCL patients at diagnosis. The patients were included in the ALCL-99 trial (NCT00006455)28. The patient’s treatment consisted of a cytoreductive prephase followed by six chemotherapy courses, as previously described33. The patient cohort consisted of 57% male, 89.2% CNS negative and 41.9% MDD positive patients. The majority of patients showed no bone marrow (94.6%), bone (82.8%) or skin (81.7) involvement at diagnosis (Table 40). Table 40 Baseline characteristics of Paediatric ALCL Patients Recruited to the ALCL99 Trial MDD = Minimal disseminated disease. Characteristic Classification Number of patients % of total Total 93 Gender Male 53 57 Female 40 43 St Jude Stage N/A 4 4.3 I 6 6.5 II 19 20.4 III 59 63.4 IV 5 5.4 Age < 10 years 27 29 >= 10 years 66 71 Histological subtype Common 53 57 Small cell, lymphohistiocytic, mixed, giant, not further classified 40 43 CNS N/A 9 9.7 negative 83 89.2 positive 1 1.1 Bone marrow No 88 94.6 Yes 5 5.4 Bone No 77 82.8 Yes 16 17.2 Skin No 76 81.7 Yes 17 18.3 MDD N/A 18 19.4 negative 36 38.7 positive 39 41.9 ALK antibodies in plasma or serum were assessed using both an immunocytochemical approach (anti- ALK autoantibody titre) as well as the newly developed microarray approach (anti-ALK autoantibody concentration). Anti-ALK autoantibodies were detected in 92.4% (86/93) and 93.5% (87/93) of patients using the immunocytochemical approach and the microarray approach, respectively. We also observed a strong correlation (ρ = 0.68, Spearman) between anti-ALK autoantibody titre and anti-ALK autoantibody concentration levels across the paediatric ALK-positive ALCL patient cohort (Figure 45). 131 Figure 45 Correlation between anti-ALK autoantibody titre and anti-ALK autoantibody concentration levels in paediatric ALK-positive ALCL patients enrolled in the ALCL-99 trial Patients that relapsed within 5 years following diagnosis are highlighted. ρ, Spearman correlation coefficient. Twenty-eight patients (30%) mounted low or no autoantibody titres against ALK (≤ 1/750) and 65 patients (70%) presented intermediate or high titres (> 1/750). In comparison, 37 patients (40%) showed a low or no anti-ALK autoantibody concentration (≤ 200) and 56 patients (60%) had intermediate or high concentrations (> 200, Figure 46). As previously reported91,39,92 for anti-ALK autoantibody titres, low anti-ALK autoantibody titres (≤ 1/750, p < 0.0001) and concentrations (≤ 200, p = 0.033) were associated with a decreasing 5-year EFS (Figure 46A), but not with OS (Figure 46B). In agreement with previous reports91,39,92 on anti-ALK autoantibody titres, the anti-ALK autoantibody titres (p = 0.0005, Gray test) and concentrations (p = 0.009, Gray test) inversely correlated with the risk of relapse (Figure 46C). Previous work reported minimal disseminated disease (MDD) detected by qualitative RT-PCR for NPM1-ALK in bone marrow or peripheral blood to confer a relapse risk of ∼50%73,79. When the MDD and antibody titres were considered in combination, the following three subgroups of patients with different prognoses were identified: (1) a biological high risk (bHR) group defined by MDD-positivity and antibody titre ⩽1/750; (2) a biological low risk (bLR) group defined by MDD-negativity and an antibody titre >1/750; (3) a biological intermediate risk (bIR) group including all other patients (MDD- negative/antibody titre ⩽1/750 or MDD-positive/antibody titre >1/750)39. NPM-ALK transcripts, analyzed by RT-qPCR of bone marrow or peripheral blood, were available for 75 patients within our cohort (Figure 47). Utilizing the same risk stratification as for anti-ALK autoantibody titre tested patients, 48% (36/75) of patients were classified as bLR, 36% (27/75) as bIR and 17% (13/75) as bHR (Figure 47C) and relapse risk was significantly different (p = 0.009) for bLR (8.3%), bIR (18.5%) and bHR (62.2%) (Figure 47C). When the MDD and antibody concentrations were considered in combination, the following three subgroups of patients with differing prognoses were identified: (1) a bHR group defined by MDD- positivity and antibody concentration ⩽350; (2) a bLR group defined by MDD-negativity and an antibody concentration >350; (3) a bIR group including all other patients (MDD-negative/antibody concentration ⩽350 or MDD-positive/antibody concentration >350). Utilizing the same risk stratification as Mussolin et al.39, 48% (36/75) of patients were classified as bLR, 21% (16/75) as bIR and 31% (23/75) as bHR (Figure 47C). Relapse risk was significantly different (p = 0.005) for bLR (8.3%), bIR (12.5%) and bHR (47.8%) (Figure 47C). Hence, these data suggest that the anti-ALK autoantibody concentration could be used for the risk stratification of patients with ALK+ ALCL. 132 Figure 46 Outcomes of paediatric ALK+ ALCL patients according to the magnitude of the antibody response to ALK (A,B) Paediatric patients (n = 93) treated with standard ALCL99 chemotherapy within the ALCL99 trial were divided into two groups according to the anti-ALK autoantibody titre (left) or the anti-ALK autoantibody concentration (right) and the difference in median (A) EFS or (B) OS (log-rank test) was analyzed using the Kaplan-Meier estimator. (C) Cumulative incidence of relapse in paediatric patients (n = 94) treated with standard ALCL99 chemotherapy within the ALCL99 trial that were divided into two groups according to the anti-ALK autoantibody titre (left) or the anti-ALK autoantibody concentration (right). P-values were determined by a Gray test. 133 Figure 47 Outcomes of paediatric ALK+ ALCL patients according to the magnitude of the antibody response against ALK in combination with their minimal disseminated disease (MDD) status (A) Cumulative incidence of relapse in MDD-negative paediatric patients (n = 36) treated with standard ALCL99 chemotherapy within the ALCL99 trial were divided into two groups according to the anti-ALK autoantibody titre (left) or the anti-ALK autoantibody concentration (right). P-values were determined by a Gray test. (B) The cumulative incidence of relapse in MDD-positive paediatric patients (n = 40) treated with standard ALCL99 chemotherapy within the ALCL99 trial were divided into two groups according to the anti-ALK autoantibody titre (left) or the anti-ALK autoantibody concentration (right). P-values were determined by a Gray test. (C) The cumulative incidence of relapse in paediatric patients (n = 76) treated with standard ALCL99 chemotherapy within the ALCL99 trial were divided into three groups according to the anti-ALK autoantibody titre (left) or the anti-ALK autoantibody concentration (right) combined with the MDD status and anti-ALK autoantibody titre or concentration of the patients. P-values were determined by a Gray test. 134 7.4 Discussion Biomarkers that predict a patient’s prognosis and/or response to therapy are informative in devising therapeutic protocols, particularly in this era of personalised medicine. Previous reports have described that the magnitude of the autoantibody response to the oncoantigen ALK is inversely correlated with lymphoma dissemination and relapse risk in ALK-positive ALCL, and that by combining MDD and antibody titer, patients could be stratified into three different groups with significantly different PFS probabilities39,91. The present study validates that the preexisting antibody response to ALK correlates inversely with tumour dissemination and has prognostic value for patients with this malignancy. The automated assay we have developed could allow for a non-subjective assessment of this biomarker for incorporation into future ALCL clinical trials. However, whether this biomarker remains predictive when children with ALK+ ALCL are treated with targeted agents such as ALK inhibitors and anti-CD30 antibody therapy remains to be seen. The increased risk of relapse in patients with low antibody titers against ALK did not translate into a significant difference in OS. One explanation for this may be the availability of an effective salvage therapy for most relapsing ALK+ ALCL patients435. In addition, our data support published data88,89,91,90,39,92 that an immune response to ALK is implicated in the control of ALK+ ALCL. Together with the observation of ALK-specific cellular immune responses in patients undergoing multi-agent chemotherapy treatment92 and in long-term survivors of ALK+ ALCL89, these data further support the idea of a specific immunostimulatory approach for the consolidation of remission in patients with ALK+ ALCL435. There is a growing body of evidence to support the potential development of an ALK targeting vaccine503; ALK ranked fourth in a list of 75 tumour antigens evaluated by the National Cancer Institute505. The combination of a vaccination against ALK with either immune stimulatory multi-agent chemotherapy432,433,434,435 or ALK TKIs436,437 protected against relapse in murine models of ALK+ ALCL and ALK+ NSCLC, respectively142,501. In support of this, anti-HER2 autoantibodies were found to suppress the activity of HER2 in HER2+ breast or ovarian cancer patients after vaccination with HER2 specific peptides435,506. Therefore, vaccination to boost a pre-existing anti-ALK immune response for ALK+ ALCL or NSCLC patients with a pre-existing ALK immune response could provide a promising approach435 without the risks associated with unspecific immunostimulatory therapies like nivolumab138, resistance-prone ALK inhibitor therapy126 or in the case of ALCL highly toxic SCT and BV. In addition, a direct anti-tumour effect of recombinant anti-ALK autoantibodies has been described in ALK-driven NB507,508 and glioblastoma509. These data suggest that recombinant anti-ALK antibodies might provide a therapeutic effect in other ALK+ cancer patients. While the anti-ALK antibody titre in ALK+ ALCL or NSCLC appears to be associated with the anti-tumour immune response435,142,140,89,510– 512, it is not clear whether they are functional against tumour cells435. Although, ALK fusion proteins in ALK+ ALCL and NSCLC patients are expressed exclusively intracellularly371,513–519, future studies by Chiarle and Woessmann will shed light on this unanswered question. Finally, as part of this chapter, we analyzed anti-ALK autoantibody titres in 124 ALK+ NSLCL patients recruited onto the ALEX trial (NCT02075840) as well as 103 ALK+ ALCL patients recruited onto the ANHL12P1 trial (NCT01979536), which will be made available once the trials have been completed. 135 CHAPTER 8 Discussion 136 8.1 Introduction Throughout this thesis, genome wide CRISPR overexpression screens in ALK+ ALCL and ALK driven NB cell lines have yielded insights into potential mechanisms of ALK inhibitor resistance. Finally, the development of a paediatric ALK+ ALCL PDX model has allowed the in vivo investigation of brigatinib treatment in a chemotherapy-refractory and crizotinib-resistant setting. In this chapter, potential future directions and the clinical implications of these findings are discussed in the context of the current literature. 8.2 The use of ALK inhibitors for the treatment of paediatric ALK+ ALCL 8.2.1 Crizotinib in combination with multi-agent chemotherapy could be used as a consolidation therapy before allogenic SCT for paediatric ALK+ ALCL patients after relapse Fortunately, paediatric ALK+ ALCL patients are relatively chemo-sensitive with an OS varying between 70-90% dependent on treatment duration, drugs used and their dosages (Table 3)31–33,42–44. In addition, four independent retrospective analyses found that approximately 75% of relapsed patients reached a second remission by reinduction chemotherapy71,97,98,118. The response rate was dependent on the time to relapse, with approximately 85% of children with relapse after completion of frontline therapy reaching a remission by any chemotherapy71,97,98,118, but approximately 50% of children who experienced progression during frontline therapy undergoing progression again during reinduction98. Fortunately, for this patient group, 5-year EFS and OS rates of 81% and 83%, respectively, can be achieved by allogenic SCT98. Crizotinib and BV are targeted therapies inducing remission in up to 90% of patients with relapsed ALK+ ALCL63,115,117,126,128,520 that could offer an alternative to highly toxic multi-agent chemotherapy protocols, but there is no consensus as to whether these drugs should be administered as single agents, for how long and for which patients. Our investigation into possible resistance mechanisms to ALK inhibitor treatment including crizotinib showed multiple options via which an ALK+ ALCL cell can acquire resistance to single agent ALK inhibitors301,521. Specifically, our results indicate that IL10RA expression does not correlate with response or resistance to standard chemotherapy, suggesting that resistance mechanisms, such as elevated IL10RA expression developing as a consequence of single agent crizotinib therapy, could be overcome by a combination of ALK-targeted therapy with chemotherapy. Single agent crizotinib could be used to induce second remission98,522 as already established in adult relapsed ALK+ ALCL patients before allogeneic SCT128. However, while single agent crizotinib represents a low toxicity option98, a combination of crizotinib with chemotherapy could be advisable to prevent ALK-inhibitor resistance-specific relapse if crizotinib has to be given for an extended time to achieve remission. This is especially important as CNS progession in crizotinib treated patients has been observed and CNS prophylaxis during re-induction therapy before SCT is highly recommended523. Low-risk patients defined by relapse at more than one year after initial diagnosis might better be treated with multi-agent chemotherapy for which many years of experience exists98. 137 8.2.2 Brigatinib could offer a bridge to transplant for paediatric ALK+ ALCL patients after CNS relapse Since the 5-year cumulative CNS relapse risk in paediatric ALK+ ALCL patients is only 4% and the 3-year OS for patients after CNS relapse is 48.7% with median survival being 23.5 months100, this sub- group of patients comprises the most difficult to treat cases with the least treatment experience. Early identification of ALK+ ALCL patients with a risk of CNS relapse is an important future goal that will enable tailored treatment strategies100. For example, lorlatinib and brigatinib have shown promising results in ALK+ NSCLC but have not yet been sufficiently tested in the paediatric setting125,524. The advantage of these ALK inhibitors over crizotinib and ceritinib is that they are able to cross the blood- brain-barrier and as such are active or preventive against CNS disease525–528. In Japan, crizotinib is being trialled as a monotherapy for children with recurrent or refractory ALK+ ALCL (UMIN000028075) and in the USA, crizotinib is being investigated in combination with multi-agent chemotherapy (NCT01979536) and might therefore not present a viable option for patients with CNS involvement366. However, in Japan, alectinib (UMIN000016991)67–69 has been approved for children with recurrent or refractory ALK+ ALCL in 2020124 and the EICNHL is planning to trial brigatinib in combination with the ALCL99 backbone (personal communication with Dr. Suzanne Turner) offering hope for paediatric ALK+ ALCL patients with CNS involvement at diagnosis or experiencing a CNS relapse. In addition, a first case report described the successful treatment of a girl, who suffered from a CNS relapse, with alectinib125. While limited to a CNS relapsed patient who achieved a CR during initial crizotinib treatment but relapsed after allogenic SCT, our in vivo investigation indicates that brigatinib was effective in a PDX model of this CNS relapsed chemotherapy-refractory and crizotinib-resistant ALK+ ALCL patient. The investigation of brigatinib in clinical trials will have to prove whether this can be translated to the clinic. 8.3 The use of ALK inhibitors for the treatment of ALK-driven NB Our investigation into possible resistance mechanisms against ALK inhibitor treatment including ceritinib and brigatinib highlighted multiple pathways through which ALK-driven NB cells can acquire resistance to single agent ALK inhibition300. Trigg et al. identified PIM1 overexpression as one of the main resistance mechanisms to ceritinib and brigatinib and further explored PIM1 as a therapeutic target after observing that its overexpression led to evasion of apoptosis300,529. While treatment with the PIM1 inhibitor AZD1208 was not sufficient to kill ALK-driven NB cells, reduction of PIM1 mRNA sensitized NB cells to ALK inhibitors and combination of AZD1208 with ALK inhibitors showed mild synergism. To examine the clinical relevance of this drug combination, Trigg et al. employed two patient-derived models of high-risk NB harboring ALKF1245C or ALKF1174L mutations, respectively300. The authors observed a significant delay in tumour growth with the combination treatment relative to single-agent treatments in both models300. PIM inhibition sensitized both MYCN-amplified and wild-type, ALK-driven NB cells to ALK inhibitors in vivo. It has previously been shown that ALK and MYCN are part of a positive feedback loop whereby ALK regulates expression of MYCN through repression of HPB1530. However, Trigg et al. suggest that combined PIM1 and ALK inhibition is effective independent of MYCN status300. 138 Moreover, PIM1 mRNA levels were significantly elevated in tumours treated with ceritinib relative to the vehicle at the experimental end-point, thus providing in vivo evidence of PIM1 as a resistance gene300. 8.4 A collaborative approach to collate and integrate data will be crucial to making progress in the treatment of paediatric cancers The low incidence of paediatric cancer cases has been a key factor in the limited collection of clinical and biological data, and the difficulties encountered in conducting clinical trials. Given this, utilizing in vitro screening of relatively uncommon paediatric tumours such as ALK+ ALCL and ALK-driven NB is currently the only approach to characterize ALK inhibitor resistance mechanisms. However, it remains unclear what proportion of clinical cases of ALK inhibitor resistance involve IL10RA in ALK+ ALCL or PIM1 in ALK-driven NB. Moreover, it remains to be elucidated what level of IL10RA expression in ALK+ ALCL or PIM1 expression in ALK-driven NB are needed in vivo for induction of these resistance mechanisms531. In our studies, IL10RA and PIM1 were artificially overexpressed in vitro and in the case of IL10RA with or without IL-10 supplementation. Ultimately, further clinical samples of matched diagnostic and relapse tumours are required to assess the degree to which IL10RA or PIM1 overexpression represents a predominant resistance mechanism to the investigated or other ALK inhibitors. The full diversity of ALK-independent escape mechanisms also remains unknown531; survival pathways other than IL10 signaling or PIM1-induced apoptosis evasion may exist and resistance could be induced by other gene candidates identified by our screening approach. This will require extensive functional follow-up studies with validation in patient tumours. Additionally, Turner532 and other groups533–560 have identified a distinct population of cells – called cancer stem cells561, tumour-initiating cells562, leukaemia-initiating cells561 or tumour-propagating cells563 – within cell lines derived from haematopoietic (including ALK+ ALCL532) and solid tumours (including NB564) that by definitions of Clarke et al.565 and Nguyen et al.566 can be isolated and were shown to have self-renewal capacity561. In future work it will be interesting to test whether the cancer stem cell population of ALK+ ALCL and NB cell lines overexpress IL10RA and PIM1, respectively, or other candidates identified by the screens. Furthermore, the factors underlying the diversity of resistance mechanisms that develop in patients with the same cancer receiving the same treatment remain unclear. Considering the annual 300,000 European childhood cancer survivors, many are at risk of relapse and of the 35,000 newly diagnosed paediatric cancer cases in Europe each year many present with refractory disease (Pilot Project & Preparation Action Grant, European Commission). Therefore, several national clinical sequencing studies aimed at identifying treatment targets in children or adolescents are based on tumour biopsies taken at relapse/refractory disease439: Individualized Therapy for Relapsed Malignancies in Childhood (INFORM, Germany), Individualized Therapies for Children with Relapsed/Refractory Malignancies using Molecular Profiling (iTHER, The Netherlands), MAPPYACTS, Zero Childhood Cancer (ZERO, Australia), Precision Oncology for Young People Program (PROFYLE, Canada) and Pediatric Molecular Analysis for Therapy Choice (Pediatric MATCH, USA). Since paediatric cancers are rare diseases, going forward, a collaborative approach to collate and integrate the collected data will be crucial and must be compared to robust biological functional validation studies as described here. 139 CHAPTER 9 Appendix 140 9.1 Appendix 1: List of peer-reviewed papers and reviews 9.1.1 Primary research articles 10 Lobello C., Boris T., Bystry V., Radova L., Filip D., Marz M., Montes-Mojarro I.A., Prokoph N., Larose H., Liang H.C., Sharma G.G., Mologni L., Belada D., Kamaradova K., Fend F., Gambacorti-Passerini C., Merkel O., Turner S.D., Janikova A., Pospisilova S. (2020) STAT3 and TP53 Mutations Associate with Poor Prognosis in Anaplastic Large Cell Lymphoma. Leukemia. 9 Forde S.D., Matthews J.D., Jahangiri L., Lee L.C., Prokoph N., Malcolm T.I.M., Giger O.T., Bell N., Blair H., O’Marcaigh A., Smith O., Kenner L., Bomken S., Burke G.A.A., Turner S.D. (2020) Paediatric burkitt lymphoma patient-derived xenografts capture disease characteristics over time and are a model for therapy. British Journal of Haematology. 8 Prokoph N., Probst N.A., Lee L.C., Monahan J.M., Matthews J.D., Liang H-C., Bahnsen K., Montes- Mojarro I.A., Karaca-Atabay E., Sharma G.G., Malik V., Larose H., Forde S.D., Ducray S.P., Lobello C., Wang Q., Pospisilova S., Gambacorti-Passerini C., Burke G.A.A., Pervez S., Attarbaschi A., Janikova A., Parquement H., Landman-Parker J., Lambilliotte A., Schleiermacher G., Klapper W., Jauch R., Woessmann W., Vassal G., Kenner L., Merkel O., Mologni L., Chiarle R., Brugières L., Geoerger B., Barbieri I., Turner S.D. (2020) IL10RA modulates crizotinib sensitivity in NPM1-ALK+ anaplastic large cell lymphoma. Blood. 136(14):1657-1669. Cover page & Invited commentary by the editor: Hu G. & Feldman A.L. (2020) Drivers of crizotinib resistance in ALK+ ALCL. Blood. 136(14):1573-1575. 7 Larose H., Prokoph N., Matthews J.D., Schlederer M., Högler S., Alsulami A.F., Ducray S.P., Nuglozeh E., Fazaludeen F.M.S., Elmouna A., Ceccon M., Mologni L., Gambacorti-Passerini C., Hoefler G., Lobello C., Pospisilova S., Janikova A., Woessmann W., Damm-Welk C., Zimmermann M., Fedorova A., Malone A., Smith O., Wasik M., Inghirami G., Lamant L., Blundell TL., Klapper W., Merkel O., Burke G.A.A., Mian S., Ashankyty I., Kenner L., Turner S.D. (2020) Whole Exome Sequencing reveals NOTCH1 mutations in Anaplastic Large Cell Lymphoma and points to Notch both as a key pathway and a potential therapeutic target. Haematologica. 6 Trigg R.*, Lee L.C.*, Prokoph N.*, Jahangiri L., Reynolds P., Burke G.A.A., Probst N.A., Han M., Matthews J.D., Lim H.K., Manners E., Martínez Gonzalez S., Pastor Fernandez J., Blanco-Aparicio C., Merkel O., Garces de los Fayos Alonso I., Kodajova P., Tangermann S., Högler S., Luo J., Kenner L., Turner S.D. (2019) The targetable kinase PIM1 drives ALK inhibitor resistance in high-risk neuroblastoma independent of MYCN status. Nat Commun. 10(1):5428. *joint first Editors’ choice: Malone J. (2019) A new hope for neuroblastoma treatment? Science Transl. Med. 11(523), eaaz9769. 5 Russell M., Prokoph N., Henderson N., Eketjäll S., Balendran C., Michaëlsson E., Fidock M., Hughes G. (2017) Determining myeloperoxidase activity and protein concentration in a single assay: utility in biomarker and therapeutic studies. Journal of Immunological Methods. 449:76-79. 4 Fontaine F., Overman J., Moustaqil M., Mamidyala S., Salim A., Narasimhan K., Prokoph N., Robertson AAB., Lua L., Alexandrov K., Koopman P., Capon RJ., Sierecki E., Gambin Y., Jauch R., Cooper MA., Zuegg J., Francois M. (2017) Small molecule inhibitors of the Sox18 transcription factor. Cell Chemical Biology. 24(3):346-359. 3 Prokoph N., Ormö M., O’Mahony G., Hogner A., McPheat J., Karlsson U., Holmberg Schiavone L., Liu J. (2016) Development of an ELISA assay for high throughput screening of Inhibitors of the CDK5-mediated PPARγ phosphorylation. Assay Drug Dev Techn. 14(4):261-72. 2 Klaus M.*, Prokoph N.*, Wang X., Huang Y.-H., Girbig M., Srivastava Y., Hou L., Narasimhan K., Kolatkar P., Francois M., Jauch R. (2016) Structure and decoy-mediated inhibition of the SOX18/Prox1-DNA interaction. Nucleic Acids Res. 44(8):3922-35. *joint first 1 Paul A.J., Schwab K., Prokoph N., Haas E., Handrick R., Hesse F. (2015) Fluorescence dye-based detection of mAb aggregates in CHO culture supernatants. Anal. Bioanal. Chem. 407(16):4849-56. 9.1.2 Review article 1 Prokoph N.*, Larose H.*, Lim M.S., Burke G.A.A., Turner S.D. (2018) Treatment Options for Paediatric Anaplastic Large Cell Lymphoma (ALCL): Current Standard and beyond. Cancers. 10(4):99. *joint first 141 Reference List 1. Chiarle R, Voena C, Ambrogio C, Piva R, Inghirami G. The anaplastic lymphoma kinase in the pathogenesis of cancer. 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