Large-scale meta–genome-wide association study reveals common genetic factors linked to radiation-induced acute toxicities across cancer types Elnaz Naderi , PhD,1,2,3 Miguel E. Aguado-Barrera, PhD,4,5 Line M. H. Schack, PhD,6,7,8 Leila Dorling, PhD,9 Tim Rattay, PhD, FRCS,10 Laura Fachal, PhD,11,12 Holly Summersgill, PhD,13 Laura Mart�ınez-Calvo, PhD,4,5 Ceilidh Welsh, MSc,14 Tom Dudding, PhD,15,16 Yasmin Odding, FRCR,17 Ana Varela-Pazos, PhD,18 Rajesh Jena, PhD,19 David J. Thomson, PhD,20,21 Roel J. H. M. Steenbakkers, PhD,2 Joe Dennis , PhD,12 Ram�on Lobato-Busto, PhD,22 Jan Alsner, PhD,6 Andy Ness, PhD,15 Chris Nutting, PhD,23 Antonio G�omez-Caama~no, PhD,5,18 Jesper G. Eriksen, PhD,6Steve J. Thomas, PhD,15 Amy M. Bates, MSc,14 Adam J. Webb, PhD,24 Ananya Choudhury, FRCR,25 Barry S. Rosenstein, PhD,26 Begona Taboada-Valladares, PhD,5,18 Carsten Herskind, PhD,27 David Azria, MD, PhD,28 David P. Dearnaley , MD,29 Dirk de Ruysscher, MD,30 Elena Sperk, MD,27 Emma Hall, PhD,31 Hilary Stobart, MSc,32 Jenny Chang-Claude, PhD,33,34 Kim De Ruyck, PhD,35 Liv Veldeman, PhD,36,37 Manuel Altabas, PhD,38 Maria Carmen De Santis, PhD,39 Marie-Pierre Farcy-Jacquet, MD,40 Marlon R. Veldwijk, PhD,27 Matthew R. Sydes, PhD,41 Matthew Parliament, MD,42 Nawaid Usmani, MD,42 Neil G. Burnet, MD,21 Petra Seibold, PhD,33 R Paul Symonds, PhD,43 Rebecca M. Elliott, MSc,25 Ren�ee Bultijnck, PhD,36,37 Sara Guti�errez-Enr�ıquez, PhD,44 Meritxell Moll�a,45 Sarah L. Gulliford, PhD,46 Sheryl Green, MBBCh,47 Tiziana Rancati, PhD,48 Victoria Reyes, PhD,38 Ana Carballo, PhD,5,18 Paula Peleteiro, PhD,5,18 Paloma Sosa-Fajardo, PhD,5,18 Chris Parker , FRCR,46 Val�erie Fonteyne, MD, PhD,37 Kerstie Johnson, MBBS, MSc,49 Maarten Lambrecht , MD, PhD,50 Ben Vanneste, MD, PhD,36,37,51 Riccardo Valdagni , MD, PhD,39,52 Alexandra Giraldo, PhD,38 M�onica Ramos, PhD,38 Brenda Diergaarde, PhD,53 Geoffrey Liu , MD, MSc,54 Suzanne M. Leal, PhD,3,55 Melvin L. K. Chua, PhD, FRCR,56,57 Miranda Pring, PhD,15 Jens Overgaard , MD, FRCR,6 Luis M. Cascallar-Caneda, MD,18 Fr�ederic Duprez, PhD,36,37 Christopher J. Talbot, PhD,24 Gillian C. Barnett , MRCP, PhD,19 Alison M. Dunning, PhD,12 Ana Vega , PhD,4,5,58 Christian Nicolaj Andreassen, PhD,6,59, Johannes A. Langendijk, PhD,2 Catharine M. L. West, PhD,60 Behrooz Z. Alizadeh, PhD,1,‡ Sarah L. Kerns , PhD, MPH,61,�,‡ on the Behalf of the Radiogenomics Consortium 1Department of Epidemiology, University Medical Center Groningen, Groningen, The Netherlands 2Department of Radiation Oncology, University Medical Center Groningen, Groningen, The Netherlands 3Center for Statistical Genetics, Gertrude H. Sergievsky Center, and the Department of Neurology, Columbia University Medical Center, New York, NY, USA 4Fundaci�on P�ublica Galega Medicina Xen�omica, Santiago de Compostela, Spain 5Instituto de Investigaci�on Sanitaria de Santiago de Compostela, Santiago de Compostela, Spain 6Department of Experimental Clinical Oncology, Aarhus University Hospital, Aarhus, Denmark 7Department of Oncology, Gødstrup Hospital, Herning, Denmark 8NIDO j Centre for Research and Education, Gødstrup Hospital, Herning, Denmark 9Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK 10Leicester Cancer Research Centre, University of Leicester, Leicester, UK 11Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK 12Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK 13Manchester Academic Health Science Centre, The Christie NHS Foundation Trust, Manchester, UK 14Department of Oncology, University of Cambridge, Cambridge, UK 15Bristol Dental School, University of Bristol, Bristol, UK 16MRC Integrative Epidemiology Unit, University of Bristol, Bristol, UK 17Bristol Cancer Institute, University Hospitals Bristol NHS Foundation Trust, Bristol, UK 18Department of Radiation Oncology, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain 19Department of Oncology, Addenbrooke’s Hospital, University of Cambridge, Cambridge, UK 20Division of Cancer Sciences, University of Manchester, Manchester, UK 21The Christie NHS Foundation Trust, Manchester, UK 22Department of Medical Physics, Complexo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain 23Head and Neck Unit, The Royal Marsden Hospital, London, UK 24Department of Genetics and Genome Biology, University of Leicester, Leicester, UK 25Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Christie Hospital, Manchester, UK 26Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA 27Department of Radiation Oncology, Universit€atsmedizin Mannheim, Medical Faculty Mannheim, Heidelberg University, Mannheim, Germany 28F�ed�eration Universitaire d’Oncologie Radioth�erapie d’Occitanie M�edit�erran�ee, D�epartement d’Oncologie Radioth�erapie, ICM Montpellier, INSERM U1194 IRCM, University of Montpellier, Montpellier, France 29Division of Radiotherapy and Imaging, The Institute of Cancer Research Department, The Royal Marsden NHS Foundation Trust, London, UK 30MAASTRO Clinic, GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, Netherlands 31Clinical Trials and Statistics Unit, The Institute of Cancer Research, London, UK 32Patient Advocate, Independent Cancer Patients’ Voice, London, UK 33Division of Cancer Epidemiology, German Cancer Research Center, Heidelberg, Germany 34University Cancer Center Hamburg, University Medical Center Hamburg-Eppendorf, Hamburg, Germany 35Departments of Basic Medical Sciences and Radiotherapy, Ghent University Hospital, Ghent, Belgium 36Department of Human Structure and Repair, Ghent University, Ghent, Belgium Received: June 7, 2023. Revised: September 18, 2023. Accepted: October 18, 2023 # The Author(s) 2023. Published by Oxford University Press. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/ licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com JNCI Cancer Spectrum, 2023, 7(6), pkad088 https://doi.org/10.1093/jncics/pkad088 Advance Access Publication Date: October 20, 2023 Article https://orcid.org/0000-0003-1671-7471 https://orcid.org/0000-0003-4591-1214 https://orcid.org/0000-0002-3954-2806 https://orcid.org/0000-0001-6512-124X https://orcid.org/0000-0002-8746-2691 https://orcid.org/0000-0002-7849-603X https://orcid.org/0000-0002-2603-7296 https://orcid.org/0000-0002-0814-8179 https://orcid.org/0000-0002-1762-5942 https://orcid.org/0000-0002-7416-5137 https://orcid.org/0000-0002-6503-0011 37Department of Radiation Oncology, Ghent University Hospital, Ghent, Belgium 38Radiation Oncology Department, Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain 39Radiation Oncology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy 40F�ed�eration Universitaire d’Oncologie Radioth�erapie d’Occitanie M�edit�erran�ee, D�epartement d’Oncologie Radioth�erapie, CHU Car�emeau, Nı̂mes, France 41MRC Clinical Trials Unit at UCL, Institute of Clinical Trials and Methodology, University College London, London, UK 42Division of Radiation Oncology, Department of Oncology, Cross Cancer Institute, University of Alberta, Edmonton, AB, Canada 43Cancer Research Centre, University of Leicester, Leicester, UK 44Hereditary Cancer Genetics Group, Vall d’Hebron Institute of Oncology, Vall d’Hebron Hospital Campus, Barcelona, Spain 45Radiation Oncology Department, Vall d’Hebron Hospital Universitari, Vall d’Hebron Barcelona Hospital Campus, Barcelona, Spain 46Department of Medical Physics and Biomedical Engineering, University College London, UK 47Department of Radiation Oncology, Icahn School of Medicine at Mount Sinai, New York, NY, USA 48Data Science Unit, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy 49Leicester Cancer Research Centre, University of Leicester, Leicester, UK 50Radiation Oncology, KU Leuven, Leuven, Belgium 51Department of Radiation Oncology (Maastro Clinic), GROW School for Oncology and Developmental Biology, Maastricht University Medical Center, Maastricht, The Netherlands 52Prostate Cancer Program, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy 53Department of Human Genetics, School of Public Health, University of Pittsburgh, UPMC Hillman Cancer Center, Pittsburgh, PA, USA 54Princess Margaret Cancer Centre, Temerty Faculty of Medicine, Dalla Lana School of Public Health, University of Toronto, Toronto, ON, Canada 55Taub Institute for Alzheimer’s Disease and the Aging Brain, Columbia University Medical Center, New York, NY, USA 56Division of Radiation Oncology, National Cancer Centre Singapore, Singapore 57Duke-NUS Medical School, Oncology Academic Clinical Programme, Singapore 58Grupo de Medicina Xen�omica, Centro de Investigaci�on Biom�edica en Red de Enfermedades Raras, Universidade de Santiago de Compostela, Santiago de Compostela, Spain 59Department of Oncology, Aarhus University Hospital, Aarhus, Denmark 60Translational Radiobiology Group, Division of Cancer Sciences, University of Manchester, Manchester Academic Health Science Centre, Christie NHS Foundation Trust Hospital, Manchester, UK 61Department of Radiation Oncology, The Medical College of Wisconsin, Milwaukee, WI, USA �Correspondence to: Sarah L. Kerns, PhD, MPH, Medical College of Wisconsin, 8701 W Watertown Plank Rd, Milwaukee, WI, USA 53226 (e-mail: skerns@mcw.edu). ‡These authors contributed equally to this work. Abstract Background: This study was designed to identify common genetic susceptibility and shared genetic variants associated with acute radiation-induced toxicity across 4 cancer types (prostate, head and neck, breast, and lung). Methods: A genome-wide association study meta-analysis was performed using 19 cohorts totaling 12 042 patients. Acute standar- dized total average toxicity (STATacute) was modelled using a generalized linear regression model for additive effect of genetic var- iants, adjusted for demographic and clinical covariates (rSTATacute). Linkage disequilibrium score regression estimated shared single-nucleotide variation (SNV—formerly SNP)–based heritability of rSTATacute in all patients and for each cancer type. Results: Shared SNV-based heritability of STATacute among all cancer types was estimated at 10% (SE¼0.02) and was higher for pros- tate (17%, SE¼ 0.07), head and neck (27%, SE¼ 0.09), and breast (16%, SE¼0.09) cancers. We identified 130 suggestive associated SNVs with rSTATacute (5.0� 10–8 .05, and were avail- able in at least 50% of samples. All tests of statistical significance were 2-sided. Linkage disequilibrium score regression, SNV- based heritability, and genetic correlation Linkage disequilibrium (LD) score regression (21) used summary statistics (�1.1 million SNVs; v2 statistics from the GWAS meta- analysis) on the LD scores across the genome. An LD score regres- sion intercept close to 1 suggests no confounding bias, whereas an inflated intercept (>1) may indicate population stratification, confounding, or model misspecifications. We filtered the included variants to the subset included in HapMap3 and excluded variants with duplicated rs-numbers, ambiguity, and MAF< 0.01. We used the default European LD score file based on the European 1000 genome reference panel. Cross-trait LD score regression estimated genetic correlation (22) for acute radiation- induced toxicities between the 4 cancer types in 1-by-1 compari- sons. The slope of the regression estimated the genetic covariance between 2 radiation-induced toxicity endpoints. Gene set and in silico tissue expression analysis MAGMA (23) gene set association analysis was implemented in FUMA (24). The gene-wide P value was calculated by combining the P value of all SNVs inside genes after accounting for LD and outliers. We allowed for a window of 10 kilobase pairs upstream and downstream of each gene to capture SNVs in nearby regula- tory regions. Next, we calculated competitive gene set P values on the gene-wide P value after accounting for gene size, gene set density, and LD between genes. We defined a gene set as statisti- cally significant if its joint P value was below the threshold corre- sponding to a false discovery rate<0.05. In silico tissue expression analysis was based on the MAGMA gene property in FUMA. The normalized gene expressions (reads per kb per million) of 53 normal tissue types were obtained from Genotype-Tissue Expression, version 8 (25). To obtain differen- tially expressed gene sets for 53 tissue types, we used the nor- malized expression (zero mean of log2 (reads per kilobase pair per millionþ 1)). Two-sided t tests were performed per gene per tissue compared with all other tissues. Genes with Bonferroni P< .05 adjusted and absolute log-fold change �0.58 were defined as a differentially expressed gene set in a given tissue. Results Patient characteristics Table 1 and Supplementary Tables 2 through 5 (available online) summarize the patient and clinical characteristics of the cohorts and the treatments received. Figure 1 shows the combined distri- bution of STATacute and rSTATacute scores for the 4 cancer types, and Supplementary Figure 1 shows the distributions for each participating cohort. Supplementary Tables 12 through 15 (avail- able online) list the covariates used in statistical analyses to derive rSTATacute. Table 1 and Supplementary Figure 1 (available online) describe the distribution of STATacute and rSTATacute per cohort. Meta-GWAS of acute radiation-induced toxicity The additive effect of more than 6 million SNVs on rSTATacute (n¼10 398) and STATacute (n¼ 11 115) was estimated. The quantile-quantile plots showed no genomic inflation, suggesting that population structure was adequately controlled using 10 principal components (PCs) and included only individuals of European ancestry (Figure 2). No SNV reached genome-wide sig- nificance, but 130 SNVs with a 5.0�10−8< P< 1.0� 10−5 spanning 25 genomic regions had a suggestive association with rSTATacute. The strongest association, with an effect size of −0.174 (P¼ 1.7� 10−7) per copy of the A allele was for rs142667902. The nearest gene to this SNV is DPPA4 (Figure 3), which encodes a protein involved in the maintenance of pluripotency in stem cells. From association analysis with STATacute, the number of suggestive SNVs decreased to 66 across 27 genomic regions, with rs113548225 displaying the strongest association, at an effect size of 0.157 (P¼ 2.2� 10−7) per copy of the A allele. Supplementary Tables 16 and 17 (available online) contain the suggestively asso- ciated SNVs and Supplementary Figure 2 (available online) dis- plays Manhattan and quintile-quintile plots. We found no genome-wide significant SNVs associated with rSTATacute or STATacute for the individual cancer sites. The sug- gestive findings are summarized in Supplementary Tables 18 through 25 (available online) and Supplementary Figures 2 and 3 (available online). SNV-based heritability and genetic correlation The LD score regression intercept close to 1 for all regression models (Table 2) confirmed negligible inflation attributable to relatedness and that observed associations were due to the poly- genic architecture of ration-induced toxicities. SNV-based herit- ability (SE) estimates for rSTATacute were 12% (0.07%) for prostate cancer, 16% (0.09%) for breast cancer, and 15% (0.09%) for head and neck cancer. The joint estimated SNV-based heritability (SE) for rSTATacute was 7% (0.09%). SNV-based heritability (SE) for STATacute was estimated as 17% (0.07%) for prostate cancer, 27% (0.09%) for head and neck cancer, and 16% (0.09%) for breast can- cer. The joint SNV-based heritability (SE) for STATacute was 10% (0.02%). SNV-based heritability estimates for STATacute and rSTATacute in lung cancer were imprecise because of small sam- ple size (SE� 0.40), precluding statistical inference. A 1-to-1 cross-cancer type joint analysis of both rSTATacute and STATacute showed no statistically significant genetic correlations between pairs of cancer types (Supplementary Table 26, available online). Gene set analysis The gene set P value was computed using the gene-based P value for 4728 curated gene sets (including canonical pathways) and 6166 gene ontology terms obtained from MsigDB, version 5.2. We 4 | JNCI Cancer Spectrum, 2023, Vol. 7, No. 6 https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data used Ensembl gene models for 19 079 genes and a Bonferroni- corrected P value threshold of 2.6� 10−6. MAGMA identified pro- tein glycosylation in Golgi as statistically significantly associated with rSTATacute in head and neck cancer (P¼ 2.4�10−6, P¼ .037 corrected). The next top-ranking pathway was RNA splicing via endonucleolytic cleavage and ligation (P¼ 5.1� 10−6, P¼ .079 cor- rected, overall rSTATacute). Detailed results of the top 10 gene sets per cancer type are shown in Supplementary Tables 27 through 31 (available online). In silico tissue expression analysis The genes related to overall rSTATacute reached statistically significant up-regulated expression in skin not sun exposed (P¼ 7.2� 10−5, P¼ .004 corrected) and skin sun exposed Figure 1. Histograms of STATacute and rSTATacute distribution per cancer type and curve of normal log distribution. STAT¼ standardized total average toxicity. Figure 2. Manhattan (left) and Q-Q (right) plots of the overall meta-analysis for STATacute and rSTATacute. Mirror. Manhattan plot: The GWAS for STATacute and rSTATacute are displayed in the top and bottom panels, respectively. The x-axis represents genomic locations, while the y-axis indicates –log10 P values for SNV associations with the outcome. Each SNV is a dot. Q-Q plot: Observed –log10 P values are on the y-axis, and expected –log10 P values are on the x-axis. Every SNV is represented as a dot, with the red line signifying the null hypothesis of no genuine association. Notable deviations from the expected P value distribution appear primarily at the tail, complemented by the k coefficients, indicating effective control of population stratification. GWAS¼ genome-wide association study; Q-Q¼quintile-quintile; SNV¼ single-nucleotide variant; STAT¼ standardized total average toxicity. E. Naderi et al. | 5 https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data (P¼4.8�10−4, P¼ .026 corrected) tissues (Figure 4). No tissue reached a significant P value in the individual cancer types, but the genes associated with acute toxicity in patients with breast and lung cancer had maximum expression in their corresponding tissues (breast mammary and lung tissues); for those with head and neck cancer, esophagus mucosa ranked as the second-most expressed tissue (Supplementary Figure 4, available online). Discussion We identified 130 suggestive SNVs underlying shared genetic sus- ceptibility to acute radiation-induced toxicity and showed that acute radiation-induced toxicity is likely to have a moderate SNV-based heritability of 10%. Higher heritability estimates within cancer types confirmed that the genetic susceptibility of acute radiation-induced toxicity is partially tissue type specific. Gene set analysis identified pathways not previously associated with acute radiation-induced toxicities that should be explored functionally and as potential targets for interventions to reduce radiation injury. The top SNV associated with rSTATacute was rs142667902, near- est to DPPA4, which encodes a nuclear factor involved in the main- tenance of pluripotency in stem cells (26). The pathogenesis of acute radiation-induced toxicity involves the turnover and transit time for pluripotent stem cells to repopulate damaged tissue; thus, a role of DPPA4 in radiation-induced toxicity is plausible. Gene set analysis identified RNA splicing via endonucleolytic cleavage and ligation associated with acute radiation-induced toxicity. Exposure to ioniz- ing radiation can disrupt the coupling of RNA splicing with gene transcription involved in DNA repair, cell-cycle control, and apopto- sis. This emerging trend sheds light on the complex cellular response to DNA damage (27). Interestingly, gene expression analy- sis estimated statistically significantly up-regulated expression in skin not sun exposed and sun exposed for genes related to overall rSTATacute. A simple interpretation is that skin is the shared organ at risk for all cancer types affected acutely by RT. In line with Fess�e et al. (28), our results suggest that skin and damage to the skin resulting from sun exposure (nonionizing radiation) may be inter- esting to explore further for the understanding the mechanism involved in the response of tissues to DNA damage. Figure 3. Locus zoom plot for the top locus associated with rSTATacute. The purple diamond shows the top single-nucleotide variant and variants in red are in linkage disequilibrium with the top single-nucleotide variant. The y-axis shows observed −log10 P values, and the x-axis shows the position across the genome, with genes mapped there. STAT¼ standardized total average toxicity Table 2. Single-nucleotide variant–based heritability of STATacute and rSTATacute overall and per cancer typea Minimum, No. Single-nucleotide variants, No. k Genomic control Mean v2 Intercept (SE) h2 (SE) STATacute Overall 7410 1 087 229 1.022 1.022 1.013 (0.006) 0.102 (0.026) Prostate cancer 2681 1 152 958 1.007 1.013 1.020 (0.006) 0.171 (0.069) Breast cancer 1890 1 067 908 1.004 1.009 1.007 (0.006) 0.161 (0.096) Head and neck cancer 2292 1 148 120 1.019 1.018 1.005 (0.005) 0.268 (0.088) Lung cancer 547 709 968 1.011 1.013 1.019 (0.007) 0.831 (0.399) rSTATacute Overall 6739 1 079 330 1.007 1.014 1.012 (0.006) 0.070 (0.028) Prostate cancer 2389 1 152 836 1.005 1.009 1.016 (0.006) 0.125 (0.076) Breast cancer 1786 1 067 908 1.007 1.009 1.012 (0.006) 0.158 (0.097) Head and neck cancer 2256 1 140 063 1.013 1.009 1.005 (0.006) 0.152 (0.091) Lung cancer 500 709 969 1.005 1.008 1.023 (0.007) 0.526 (0.424) ah2¼ single-nucleotide variation–based heritability; intercept¼protects against bias from population stratification and cryptic relatedness; STAT¼ standardized total average toxicity. 6 | JNCI Cancer Spectrum, 2023, Vol. 7, No. 6 https://academic.oup.com/jncics/article-lookup/doi/10.1093/jncics/pkad088#supplementary-data We found 95 suggestive SNVs located in 21 genomic regions associated with acute toxicity in patients with prostate cancer. The top SNV (rs72954279) was near to a pseudogene OACYLP. A transancestry meta-GWAS identified rs35283980 mapped to OACYLP associated with susceptibility to prostate cancer (29), though no studies have been published investigating a role in normal tissue radiation-induced toxicity. The top gene set in prostate cancer was “adrenergic receptor activity.” Adrenergic receptors are found throughout the body in many cell types. The bladder is particularly rich in these receptors, which are func- tionally important regulators of the activities of muscles. Pharmacomechanical and molecular approaches have revealed roles for the b(3)-adrenoceptor in the urinary bladder and gastro- intestinal tract smooth muscle, both organs susceptible to acute radiation-induced toxicity during prostate cancer treatment (30). Pullikuth et al. showed that adrenergic receptor signaling regu- lates tumor response to ionizing radiation (31), and our finding suggests that it would be worthwhile to investigate a role in nor- mal tissue responses. Given that activity of the receptor would affect multiple tissue types, it is a good candidate for further study. There were 83 suggestive SNVs in 21 genomic regions associ- ated with acute radiation-induced toxicity in patients with head and neck cancer. The top SNV, rs137992872, mapped to TCF20, encoding a widely expressed transcriptional co-regulator. Our analyses suggested that protein glycosylation in Golgi is a potential mechanism involved in susceptibility to acute radiation-induced toxicity in head and neck cancer. Approximately half of all pro- teins undergo glycosylation, and this modification has a substan- tial impact on diverse cellular processes in all tissue types. Published studies linked up-regulation of glycosylation genes with radioresistance (32,33). Inhibition of glycosylation has also been shown to enhance sensitivity to cisplatin (a DNA damaging agent) in head and neck cancer cells (34). Toth et al (32) found that plasma protein glycosylation changes in response to partial body irradiation, and the effects last during follow-up. Of 26 SNVs in 14 genomic regions possibly associated with acute radiation-induced toxicity among breast cancer cohorts, the top SNV was rs16882722, mapped near the tumor suppressor UNC5D, a netrin receptor involved in apoptosis (33). Moelans et al. observed an association between DUSP26 and UNC5D loss and chemo-RT resistance, which predicted worse survival in patients with breast cancer (35). The top gene set associated with radiation-induced toxicity in breast cancer was natural killer cell lectin-like receptor binding. Natural killer cells are innate immune cells that can respond to inflammatory signals such as interfer- ons and interleukins present at the site of normal tissue injury; they can potentiate vascular damage (36,37), and our findings suggest that it would be worthwhile to investigate their role in the pathogenesis of radiation-induced toxicity in breast cancer. We found 30 SNVs in 10 genomic regions suggestive of an association with acute radiation-induced toxicity in patients with lung cancer. The nearest gene to the top SNV (rs1471101) was MLLT3. Ayako et al. found a joint effect of the MLLT1 and MLLT3 genes on the ATM-signaling pathway and a role in repressing genotoxic stresses because of DNA double-strand breaks and maintaining genome integrity (38). Furthermore, our analysis showed the highest expression of genes in lung tissue among all 53 tissues tested and that the top gene set in radiation-induced toxicity in lung cancer was Debiasi apoptosis by reovirus infection dn. Our findings suggest that comparing the mechanisms of reovirus-induced apoptosis with radiation-induced apoptosis could identify similarities in tissue damage pathogenesis. Our observations highlight the complexity of radiation- induced toxicity and suggest new avenues to increase under- standing of the pathogenesis of acute radiation-induced toxicity in a tissue-specific manner. The bioinformatic analyses can point to potential mechanisms but should be used for hypothesis gen- erating and must be followed up with subsequent functional vali- dation studies. Validation studies and subsequent functional characterization of radiation-induced toxicity–associated SNVs in cell lines and animal models will be important next steps to Figure 4. Tissue expression analysis in 53 tissue types for genes related to overall rSTATacute. Tissue expression analysis testing the positive relationship between all annotated genes using the full distribution of single-nucleotide variant P values and the average expression of genes per tissue type based on Genotype-Tissue Expression RNA sequencing data. DEG¼differentially expressed gene set; STAT¼ standardized total average toxicity. E. Naderi et al. | 7 understand the molecular mechanisms involved and, poten- tially, identify targetable pathways for intervention. Our first estimate of shared SNV-based heritability of acute radiation-induced toxicity across 4 cancer types was 10%. The estimates were higher for prostate (17%), breast (16%), and head and neck (27%) cancers. These SNV-based heritability estimates are comparable with those for complex traits such as coronary artery disease (5%) (39), autism spectrum disorder (12%) (40), and schizophrenia (26%) (41). SNV-based heritability estimates depend on study size; thus, our estimates will improve with larger studies (42). Also, narrow-sense heritability used here misses heritability because of rare variants with large effects that are not tagged by common SNVs and to nonadditive genetic variation or epigenetic factors (43). Therefore, it is likely that the level of heritability of acute radiation-induced toxicity will be higher than that reported in our study. No SNV achieved the stringent threshold for genome-wide sig- nificance, which is a challenge in GWAS (44). The rigorous correc- tion for many statistical tests reduces false positives but may mask real associations. A second limitation is the lack of ances- tral diversity in our cohorts because of limited statistical power to perform a multiethnic GWAS; this limits the generalizability of our findings to non-European and admixed populations. Future studies should be conducted on extended sample sizes, with par- ticular effort devoted to building cohorts in non-European patient populations and more precisely defining phenotypes for radiation-induced toxicities. Although we examined common SNVs with a MAF greater than 1%, investigating the rare variants would provide significant insights. Many common variants are potentially associated with acute radiation-induced toxicities across tumor sites, and it is worth- while to carry out larger studies that have the statistical power to identify the causal variants. Our large meta-GWAS provides the first evidence for the heritability of common genetic variants associated with acute radiation-induced toxicity, which is higher within than across tissues. Further investigation to verify and expand our findings is merited to identify multiple genome-wide significant loci, with pooled clinically relevant effect sizes that can be used in clinical practice. Data availability This study was done using cohorts involved in the Radiogenomics Consortium. Summary statistics for GWAS results will be available to download from the GWAS Catalog. Author contributions Elnaz Naderi, PhD (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing), Val�erie Fonteyne, MD, PhD (Data curation; Formal analysis; Methodology; Project administration; Writing—review & editing), Chris Parker, FRCR (Conceptualization; Methodology; Project administration; Validation; Writing—review & editing), Paloma Sosa-Fajardo, PhD (Data curation; Investigation; Validation; Writing—review & editing), Paula Peleteiro, PhD (Data curation; Validation; Writing—review & editing), Ana Carballo, PhD (Investigation; Validation; Writing—review & editing), Victoria Reyes, PhD (Data curation; Project administration; Resources; Writing—review & editing), Tiziana Rancati, PhD (Data curation; Investigation; Resources; Writing—review & editing), Sheryl Green, MBBCh (Data curation; Resources; Validation; Writing— review & editing), Sarah L. Gulliford, PhD (Conceptualization; Methodology; Project administration; Writing—review & editing), Meritxell Moll�a, MD (Data curation; Writing—review & editing), Sara Guti�errez-Enr�ıquez, PhD (Data curation; Investigation; Resources; Writing—review & editing), Ren�ee Bultijnck, PhD (Data curation; Formal analysis; Validation; Visualization; Writing—review & editing), Rebecca M. Elliott, MSc (Conceptualization; Data curation; Project administration; Resources; Writing—review & editing), R. Paul Symonds, PhD (Data curation; Resources; Supervision; Writing—review & edit- ing), Petra Seibold, PhD (Formal analysis; Investigation; Validation; Writing—review & editing), Neil G. Burnet, MD (Conceptualization; Methodology; Resources; Writing—review & editing), Nawaid Usmani, MD (Data curation; Resources; Visualization; Writing—review & editing), Kerstie Johnson, MBBS MSc (Conceptualization; Resources; Validation; Writing—review & editing), Matthew Parliament, MD (Conceptualization; Resources; Validation; Writing—review & editing), Maarten Lambrecht, MD PhD (Conceptualization; Resources; Validation; Writing—review & editing), Riccardo Valdagni, MD PhD (Conceptualization; Data curation; Resources; Writing—review & editing), Catharine M. L. West, PhD (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Resources; Supervision; Validation; Writing— review & editing), Johannes A. Langendijk, PhD (Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing—review & editing), Christian Nicolaj Andreassen, PhD (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing—review & editing), Ana Vega, PhD (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing—review & editing), Alison M. Dunning, PhD (Conceptualization; Data curation; Formal analy- sis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing— review & editing), Gillian C. Barnett, PhD (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Resources; Supervision; Validation; Writing—review & editing), Christopher J. Talbot, PhD (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project administration; Resources; Supervision; Validation; Writing—review & editing), Fr�ederic Duprez, PhD (Conceptualization; Data curation; Funding acquisition; Investigation; Methodology; Supervision; Validation; Visualization; Writing—review & editing), Luis M. Cascallar- Caneda, MD (Conceptualization; Data curation; Formal analysis; Methodology; Supervision; Validation; Writing—review & edit- ing), Jens Overgaard, MD FRCR (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Supervision; Validation; Writing—review & edit- ing), Miranda Pring, PhD (Conceptualization; Data curation; Methodology; Validation; Writing—review & editing), Melvin L. K. Chua, PhD FRCR (Conceptualization; Investigation; Methodology; Project administration; Supervision; Writing—review & editing), Suzanne M. Leal, PhD (Conceptualization; Data curation; Methodology; Validation; Writing—review & editing), Geoffrey Liu, MD MSc (Conceptualization; Formal analysis; Methodology; Writing—review & editing), Brenda Diergaarde, PhD (Conceptualization; Formal analysis; Methodology; Writing— 8 | JNCI Cancer Spectrum, 2023, Vol. 7, No. 6 review & editing), M�onica Ramos, PhD (Conceptualization; Formal analysis; Validation; Writing—review & editing), Alexandra Giraldo, PhD (Conceptualization; Resources; Validation; Writing—review & editing), Ben Vanneste, MD PhD (Conceptualization; Project administration; Supervision; Writing—review & editing), Matthew R. Sydes, PhD (Conceptualization; Formal analysis; Project administration; Writing—review & editing), Marlon R. Veldwijk, PhD (Data cura- tion; Resources; Validation; Writing—review & editing), Marie- Pierre Farcy-Jacquet, MD (Data curation; Formal analysis; Methodology; Writing—review & editing), Jan Alsner, PhD (Data curation; Project administration; Writing—review & editing), Ram�on Lobato-Busto, PhD (Data curation; Resources; Writing— review & editing), Joe Dennis, PhD (Methodology; Software; Writing—review & editing), Roel J. H. M. Steenbakkers, PhD (Data curation; Resources; Writing—review & editing), David J. Thomson, PhD (Data curation; Resources; Writing—review & editing), Rajesh Jena, PhD (Data curation; Project administration; Resources; Writing—review & editing), Ana Varela-Pazos, PhD (Data curation; Resources; Writing—review & editing), Yasmin Odding, FRCR (Data curation; Investigation; Writing—review & editing), Tom Dudding, PhD (Data curation; Methodology; Software; Writing—original draft; Writing—review & editing), Ceilidh Welsh, MSc (Data curation; Methodology; Software; Validation; Writing—original draft; Writing—review & editing), Laura Mart�ınez-Calvo, PhD (Data curation; Methodology; Writing—review & editing), Holly Summersgill, PhD (Data cura- tion; Formal analysis; Project administration; Writing—review & editing), Laura Fachal, PhD (Data curation; Formal analysis; Writing—original draft; Writing—review & editing), Tim Rattay, PhD FRCS (Data curation; Formal analysis; Visualization; Writing—original draft; Writing—review & editing), Leila Dorling, PhD (Formal analysis; Validation; Writing—original draft; Writing—review & editing), Line M. H. Schack, PhD (Data cura- tion; Methodology; Resources; Validation; Writing—original draft; Writing—review & editing), Miguel E. Aguado-Barrera, PhD (Software; Writing—original draft; Writing—review & editing), Andy Ness, PhD (Project administration; Resources; Writing— review & editing), Chris Nutting, PhD (Conceptualization; Data curation; Resources; Writing—review & editing), Antonio G�omez- Caama~no, PhD (Data curation; Resources; Writing—review & editing), Jesper G. Eriksen, PhD (Data curation; Project adminis- tration; Resources; Writing—review & editing), Maria Carmen De Santis, PhD (Data curation; Investigation; Resources; Writing— review & editing), Manuel Altabas, PhD (Investigation; Resources; Writing—review & editing), Liv Veldeman, PhD (Data curation; Resources; Supervision; Writing—review & editing), Kim De Ruyck, PhD (Data curation; Methodology; Supervision; Writing— review & editing), Jenny Chang-Claude, PhD (Conceptualization; Formal analysis; Project administration; Writing—review & edit- ing), Hilary Stobart, MSc (Data curation; Investigation; Writing— review & editing), Emma Hall, PhD (Data curation; Project admin- istration; Supervision; Writing—review & editing), Elena Sperk, MD (Formal analysis; Resources; Writing—review & editing), Behrooz Z. Alizadeh, PhD (Conceptualization; Data curation; Formal analysis; Investigation; Methodology; Project administra- tion; Resources; Supervision; Validation; Visualization; Writing— original draft; Writing—review & editing), Dirk de Ruysscher, PhD (Investigation; Methodology; Writing—review & editing), David Azria, MD, PhD (Data curation; Investigation; Supervision; Writing—review & editing), Carsten Herskind, PhD (Investigation; Methodology; Validation; Writing—review & editing), Begona Taboada-Valladares, PhD (Data curation; Methodology; Resources; Writing—review & editing), Barry S. Rosenstein, PhD (Conceptualization; Data curation; Investigation; Resources; Writing—review & editing), Ananya Choudhury, FRCR (Data curation; Supervision; Writing—review & editing), Adam J. Webb, PhD (Investigation; Resources; Writing—review & editing), Amy Bates, MSc (Methodology; Validation; Writing—review & editing), Steve J. Thomas, PhD (Conceptualization; Resources; Supervision; Writing—review & editing), David P. Dearnaley, MD (Data cura- tion; Project administration; Writing—review & editing), Sarah L Kerns, PhD (Conceptualization; Data curation; Formal analysis; Funding acquisition; Investigation; Methodology; Project admin- istration; Resources; Software; Supervision; Validation; Visualization; Writing—original draft; Writing—review & editing) Funding E.N. was supported by a scholarship for a PhD from the University of Groningen, Groningen, The Netherlands. T.D. is funded as an Academic Clinical Fellow by the National Institute for Health Research, UK. D.J.T. is supported by a grant from The Taylor Family Foundation and Cancer Research UK [C19941/ A30286]. M.L.K.C. is supported by the National Medical Research Council Singapore Clinician Scientist Award (NMRC/CSA-INV/ 0027/2018), National Research Foundation Proton Competitive Research Program (NRF-CRP17-2017-05), Ministry of Education Tier 3 Academic Research Fund (MOE2016-T3-1-004), the Duke- NUS Oncology Academic Program Goh Foundation Proton Research Programme, NCCS Cancer Fund, and the Kua Hong Pak Head and Neck Cancer Research Programme. G.C.B. is supported by Cancer research UK RadNet Cambridge [C17918/A28870]. RADIOGEN research was supported by Spanish Instituto de Salud Carlos III (ISCIII) funding, an initiative of the Spanish Ministry of Economy and Innovation partially supported by European Regional Development FEDER Funds (INT20/00071, INT15/00070, INT17/00133, INT16/00154; PI19/01424; PI16/00046; PI13/02030; PI10/00164); by AECC grant PRYES211091VEGA and through the Autonomous Government of Galicia (Consolidation and structur- ing program: IN607B). C.N.A. and L.M.H.S. received funding from the Danish Cancer Society (grant R231-A14074-B2537). T.R. was funded by a National Institutes of Health Research (NIHR) Clinical Lectureship (CL 2017-11-002) and is supported by the NIHR Leicester Biomedical Research Centre. This publication presents independent research funded by the NIHR. The views expressed are those of the authors and not necessarily those of the NHS, the NIHR or the Department of Health. REQUITE received funding from the European Union’s Seventh Framework Programme for research, technological development, and dem- onstration under grant agreement No. 601826. S.G.E. is supported by the government of Catalonia 2021SGR01112. L.D. was sup- ported by the European Union Horizon 2020 research and innova- tion programs BRIDGES (grant No. 634935). Conflicts of interest The authors declare no conflicts of interest. No one was paid to write this article by a pharmaceutical company or agency. Acknowledgements The study sponsors were not involved in the design of the study; the collection, analysis, and interpretation of the data; the writ- ing of the manuscript; or the decision to submit the manuscript for publication. E. Naderi et al. | 9 We thank all patients who participated in the study and the par- ticipating clinic staff for their contribution to data collection. This publication presents data from the Head and Neck 5000 study. The study was a component of independent research funded by the NIHR under its Programme Grants for Applied Research scheme (RP-PG-0707-10034). The views expressed in this publication are those of the authors and not necessarily those of the NHS, the NIHR, or the Department of Health. Core funding was also provided through awards from Above and Beyond, University Hospitals Bristol and Weston Research Capability Funding, and the NIHR Senior Investigator award to Professor Andy Ness. Genotyping was funded by World Cancer Research Fund Pilot Grant (grant No. 2018/1792), Above and Beyond, Wellcome Trust Research Training Fellowship (201237/ Z/16/Z), and Cancer Research UK Cancer Research UK Programme Grant, the Integrative Cancer Epidemiology Programme (grant No. C18281/A19169). The VHIO authors acknowledge the Cellex Foundation for providing research equip- ment and facilities and thank CERCA Program/Generalitat de Catalunya for institutional support. References 01. Baskar R, Lee KA, Yeo R, Yeoh KW. Cancer and radiation ther- apy: current advances and future directions. Int J Med Sci 2012;9 (3):193-199. doi:10.7150/ijms.3635. 02. Dragun AE. Encyclopedia of Radiation Oncology. Heidelberg, Germany: Springer Berlin, Heidelberg; 2013. doi: 10.1007/978-3-540-85516-3. 03. Bentzen SM, Overgaard J. Patient-to-patient variability in the expression of radiation-induced normal tissue injury. Semin Radiat Oncol. 1994;4(2):68-80. doi:10.1016/S1053-4296(05) 80034-7. 04. Andreassen CN, Alsner J, Overgaard J. Does variability in normal tissue reactions after radiotherapy have a genetic basis—where and how to look for it? Radiother Oncol. 2002;64(2):131-140. doi: 10.1016/S0167-8140(02)00154-8. 05. Barnett GC, Thompson D, Fachal L, et al. A genome wide associ- ation study (GWAS) providing evidence of an association between common genetic variants and late radiotherapy toxic- ity. Radiother Oncol. 2014;111(2):178-185. doi:10.1016/j. radonc.2014.02.012. 06. Naderi E, Petra A, Crijns G, et al. A two - stage genome - wide association study of radiation - induced acute toxicity in head and neck cancer. J Transl Med 2021;19(1):481. doi: 10.1186/s12967-021-03145-1. 07. Fachal L, G�omez-Caama~no A, Barnett GC, et al. A three-stage genome-wide association study identifies a susceptibility locus for late radiotherapy toxicity at 2q24.1. Nat Genet. 2014;46(8): 891-894. doi:10.1038/ng.3020. 08. Kerns SL, Fachal L, Dorling L, et al. Radiogenomics consortium genome-wide association study meta-analysis of late toxicity after prostate cancer radiotherapy. J Natl Cancer Inst. 2020;112 (2):179-190. doi:10.1093/jnci/djz075. 09. Deichaite I, Hopper A, Krockenberger L, et al. Germline genetic biomarkers to stratify patients for personalized radiation treatment. J Transl Med. 2022;20(1):360. doi: 10.1186/s12967-022-03561-x. 10. Kerns SL, Dorling L, Fachal L, et al.; Radiogenomics Consortium. Meta-analysis of genome wide association studies identifies genetic markers of late toxicity following radiotherapy for prostate cancer. EBioMedicine. 2016;10:150-163. doi:10.1016/j. ebiom.2016.07.022. 11. De Ruyck K, Duprez F, Werbrouck J, et al. A predictive model for dysphagia following IMRT for head and neck cancer: introduc- tion of the EMLasso technique. Radiother Oncol. 2013;107(3): 295-299. doi:10.1016/j.radonc.2013.03.021. 12. Werbrouck J, De Ruyck K, Duprez F, et al. Acute normal tissue reactions in head-and-neck cancer patients treated with IMRT: Influence of dose and association with genetic polymorphisms in DNA DSB repair genes. Int J Radiat Oncol Biol Phys. 2009;73(4): 1187-1195. doi:10.1016/j.ijrobp.2008.08.073. 13. Naderi E, Schack LMH, Welsh C, et al. On the behalf of the Radiogenomics Consortium. Meta-GWAS identifies the herit- ability of acute radiation-induced toxicities in head and neck cancer. Radiother Oncol. 2022;176:138-148. doi:10.1016/j. radonc.2022.09.016. 14. Schack LMH, Naderi E, Fachal L, et al.; Danish Head and Neck Cancer Group (DAHANCA). A genome-wide association study of radiotherapy induced toxicity in head and neck cancer patients identifies a susceptibility locus associated with mucositis. Br J Cancer. 2022;126(7):1082-1090. doi:10.1038/s41416-021-01670-w. 15. Guo Z, Shu Y, Zhou H, Zhang W, Wang H. Radiogenomics helps to achieve personalized therapy by evaluating patient responses to radiation treatment. Carcinogenesis. 2015;36(3): 307-317. doi:10.1093/carcin/bgv007. 16. Barnett GC, West CML, Coles CE, et al. Standardized total aver- age toxicity score: a scale- and grade-independent measure of late radiotherapy toxicity to facilitate pooling of data from dif- ferent studies. Int J Radiat Oncol Biol Phys 2012;82(3):1065-1074. doi:10.1016/j.ijrobp.2011.03.015. 17. Purcell S, Neale B, Todd-Brown K, et al. PLINK: A tool set for whole-genome association and population-based linkage analy- ses. Am J Hum Genet 2007;81(3):559-575. doi:10.1086/519795. 18. Marchini J, Howie B, Myers S, McVean G, Donnelly P. A new mul- tipoint method for genome-wide association studies by imputa- tion of genotypes. Nat Genet. 2007;39(7):906-913. doi: 10.1038/ng2088. 19. Ani A, van der Most PJ, Snieder H, Vaez A, Nolte IM. GWASinspector: comprehensive quality control of genome- wide association study results. Bioinformatics. 2021;37(1): 129-130. doi:10.1093/bioinformatics/btaa1084. 20. Willer CJ, Li Y, Abecasis GR. METAL: Fast and efficient meta- analysis of genomewide association scans. Bioinformatics. 2010; 26(17):2190-2191. doi:10.1093/bioinformatics/btq340. 21. Bulik-Sullivan B, Loh PR, Finucane HK, et al.; Schizophrenia Working Group of the Psychiatric Genomics Consortium. LD score regression distinguishes confounding from polygenicity in genome-wide association studies. Nat Genet. 2015;47(3):291-295. doi:10.1038/ng.3211. 22. Bulik-Sullivan B, Finucane HK, Anttila V, et al.; Genetic Consortium for Anorexia Nervosa of the Wellcome Trust Case Control Consortium 3. An atlas of genetic correlations across human diseases and traits. Nat Genet. 2015;47(11):1236-1241. doi:10.1038/ng.3406. 23. de Leeuw CA, Mooij JM, Heskes T, Posthuma D. MAGMA: gener- alized gene-set analysis of GWAS data. PLoS Comput Biol. 2015;11 (4):e1004219. doi:10.1371/journal.pcbi.1004219. 24. Watanabe K, Taskesen E, Van Bochoven A, Posthuma D. Functional mapping and annotation of genetic associations with FUMA. Nat Commun. 2017;8(1):1826. doi:10.1038/s41467-017-01261-5. 25. Manuscript A.; The GTEx Consortium. The Genotype-Tissue Expression (GTEx) project. Nat Genet. 2013;45(6):580-585. doi: 10.1038/ng.2653. 10 | JNCI Cancer Spectrum, 2023, Vol. 7, No. 6 https://doi.org/10.7150/ijms.3635 https://doi.org/10.1007/978-3-540-85516-3 https://doi.org/10.1016/S1053-4296(05)80034-7 https://doi.org/10.1016/S1053-4296(05)80034-7 https://doi.org/10.1016/S0167-8140(02)00154-8 https://doi.org/10.1016/j.radonc.2014.02.012 https://doi.org/10.1016/j.radonc.2014.02.012 https://doi.org/10.1186/s12967-021-03145-1 https://doi.org/10.1038/ng.3020 https://doi.org/10.1093/jnci/djz075 https://doi.org/10.1186/s12967-022-03561-x https://doi.org/10.1016/j.ebiom.2016.07.022 https://doi.org/10.1016/j.ebiom.2016.07.022 https://doi.org/10.1016/j.radonc.2013.03.021 https://doi.org/10.1016/j.ijrobp.2008.08.073 https://doi.org/10.1016/j.radonc.2022.09.016 https://doi.org/10.1016/j.radonc.2022.09.016 https://doi.org/10.1038/s41416-021-01670-w https://doi.org/10.1093/carcin/bgv007 https://doi.org/10.1016/j.ijrobp.2011.03.015 https://doi.org/10.1086/519795 https://doi.org/10.1038/ng2088 https://doi.org/10.1093/bioinformatics/btaa1084 https://doi.org/10.1093/bioinformatics/btq340 https://doi.org/10.1038/ng.3211 https://doi.org/10.1038/ng.3406 https://doi.org/10.1371/journal.pcbi.1004219 https://doi.org/10.1038/s41467-017-01261-5 https://doi.org/10.1038/ng.2653 26. Maldonado-Saldivia J, van den Bergen J, Krouskos M, et al. Dppa2 and Dppa4 are closely linked SAP motif genes restricted to pluripotent cells and the germ line. Stem Cells. 2007;25(1): 19-28. doi:10.1634/stemcells.2006-0269. 27. Shkreta L, Chabot B. The RNA splicing response to DNA damage. Biomolecules. 2015;5(4):2935-2977. doi:10.3390/biom5042935. 28. Fess�e P, Qvarnstr€om F, Nyman J, Hermansson I, Ahlgren J, Turesson I. UV-radiation response proteins reveal undifferenti- ated cutaneous interfollicular melanocytes with hyperradio- sensitivity to differentiation at 0.05 Gy radiotherapy dose fractions. Radiat Res. 2019;191(1):93-106. doi:10.1667/RR15078.1 29. Conti DV, Darst BF, Moss LC, et al. Trans-ancestry genome-wide association meta-analysis of prostate cancer identifies new sus- ceptibility loci and informs genetic risk prediction. Nat Genet. 2021;53(3):413. doi:10.1038/s41588-020-00748-0. 30. Tanaka Y, Horinouchi T, Koike K. New insights into beta- adrenoceptors in smooth muscle: Distribution of receptor sub- types and molecular mechanisms triggering muscle relaxation. Clin Exp Pharmacol Physiol. 2005;32(7):503-514. doi: 10.1111/j.1440-1681.2005.04222.x. 31. Hassan S, Pullikuth A, Nelson KC, et al. b2-adrenoreceptor sig- naling increases therapy resistance in prostate cancer by upre- gulating MCL1. Mol Cancer Res. 2020;18(12):1839-1848. doi: 10.1158/1541-7786.MCR-19-1037 32. T�oth E, V�ekey K, Ozohanics O, et al. Changes of protein glycosy- lation in the course of radiotherapy. J Pharm Biomed Anal. 2016; 118:380-386. doi:10.1016/j.jpba.2015.11.010. 33. Zhu Y, Yu M, Chen Y, et al. Down-regulation of UNC5D in blad- der cancer: UNC5D as a possible mediator of cisplatin induced apoptosis in bladder cancer cells. J Urol. 2014;192(2):575-582. doi:10.1016/j.juro.2014.01.108. 34. Noda I, Fujieda S, Seki M, et al. Inhibition of N-linked glycosyla- tion by tunicamycin enhances sensitivity to cisplatin in human head-and-neck carcinoma cells. Int J Cancer. 1999;80(2):279-284. doi:10.1002/(sici)1097-0215(19990118)80:2<279::aid-ijc18>3.0. co;2-n. 35. Moelans CB, van Maldegem CMG, van der Wall E, van Diest PJ. Copy number changes at 8p11-12 predict adverse clinical out- come and chemo- and radiotherapy response in breast cancer. Oncotarget. 2018;9(24):17078-17092. doi:10.18632/oncotar- get.24904. 36. Vivier E, Tomasello E, Baratin M, Walzer T, Ugolini S. Functions of natural killer cells. Nat Immunol. 2008;9(5):503-510. doi: 10.1038/ni1582. 37. M€unz C. Natural killer cells and autoimmunity. Nat Kill Cells. 2010;5:461-467. doi:10.1016/B978-0-12-370454-2.00034-X 38. Ui A, Yasui A. Collaboration of MLLT1/ENL, polycomb and ATM for transcription and genome integrity. Nucleus. 2016;7(2): 138-145. doi:10.1080/19491034.2016.1177681 39. Chen H, Wang T, Yang J, Huang S, Zeng P. Improved detection of potentially pleiotropic genes in coronary artery disease and chronic kidney disease using GWAS summary statistics. Front Genet 2020;11:592461. doi:10.3389/fgene.2020.592461. 40. Grove J, Ripke S, Als TD, et al.; 23andMe Research Team Identification of common genetic risk variants for autism spectrum disorder. Nat Genet. 2019;51(3):431-444. doi: 10.1038/s41588-019-0344-8 41. Ripke S, Neale BM, Corvin A, et al. Biological insights from 108 schizophrenia-associated genetic loci. Nature. 2014;511:421-427. doi:10.1038/nature13595. 42. Evans LM, Tahmasbi R, Vrieze SI, et al.; Haplotype Reference Consortium Comparison of methods that use whole genome data to estimate the heritability and genetic architecture of complex traits. Nat Genet. 2018;50(5):737-745. doi: 10.1038/s41588-018-0108-x. 43. Yang J, Zeng J, Goddard ME, Wray NR, Visscher PM. Concepts, estimation and interpretation of SNP-based heritability. Nat Genet. 2017;49(9):1304-1310. doi:10.1038/ng.3941. 44. Visscher PM, Wray NR, Zhang Q, et al. 10 Years of GWAS discov- ery: biology, function, and translation. Am J Hum Genet. 2017;101 (1):5-22. doi:10.1016/j.ajhg.2017.06.005. E. Naderi et al. | 11 https://doi.org/10.1634/stemcells.2006-0269 https://doi.org/10.3390/biom5042935 https://doi.org/10.1667/RR15078.1 https://doi.org/10.1038/s41588-020-00748-0 https://doi.org/10.1111/j.1440-1681.2005.04222.x https://doi.org/10.1158/1541-7786.MCR-19-1037 https://doi.org/10.1016/j.jpba.2015.11.010 https://doi.org/10.1016/j.juro.2014.01.108 https://doi.org/10.1002/(sici)1097-0215(19990118)80:2279::aid-ijc183.0.co;2-n https://doi.org/10.1002/(sici)1097-0215(19990118)80:2279::aid-ijc183.0.co;2-n https://doi.org/10.18632/oncotarget.24904 https://doi.org/10.18632/oncotarget.24904 https://doi.org/10.1038/ni1582 https://doi.org/10.1016/B978-0-12-370454-2.00034-X https://doi.org/10.1080/19491034.2016.1177681 https://doi.org/10.3389/fgene.2020.592461 https://doi.org/10.1038/s41588-019-0344-8 https://doi.org/10.1038/nature13595 https://doi.org/10.1038/s41588-018-0108-x https://doi.org/10.1038/ng.3941 https://doi.org/10.1016/j.ajhg.2017.06.005 Active Content List Methods Results Discussion Author contributions Conflicts of interest Acknowledgements References