Hepatocellular carcinoma (HCC) can have viral or non-viral causes
In hepatocellular carcinoma driven by non-alcoholic steatohepatitis, aberrant T cell activation and impaired immune surveillance seem to make hepatocellular carcinoma less responsive to anti-PD1 or anti-PDL1 immunotherapy.
corrected publication 2021
Potentially curative treatments for HCC, such as liver transplantation, tumour resection, or ablation, are limited to early-stage tumours
We fed mice with diets that cause progressive liver damage and NASH over 3–12 months (Extended Data Fig.
The high numbers of T cells in NASH suggest that anti-PD1-targeted immunotherapy may serve as an efficient therapy for NASH–HCC. Thirty per cent of C57BL/6 mice fed a choline-deficient high-fat diet (CD-HFD) for 13 months developed liver tumours with a similar load of genetic alterations to human NAFLD–HCC or NASH–HCC (Extended Data Fig.
As CD8+PD1+ T cells failed to execute effective immune surveillance, but rather showed tissue-damaging potential, we reasoned that CD8+ T cells might be involved in promoting NASH–HCC. We depleted CD8+ T cells in a preventive setting in mice with NASH but without liver cancer (CD-HFD fed for 10 months). CD8+ T cell depletion significantly decreased liver damage and the incidence of HCC in these mice (Fig.
We used an immune-mediated cancer field (ICF) gene-expression signature associated with the development of human HCC
We next analysed the hepatic T cell compartment for correlations with inflammation and hepatocarcinogenesis. Comparison of CD8+PD1+ T cells with CD8+ T cells by scRNA-seq showed that the former showed higher expression of genes associated with effector function (for example, increased
We characterized the transcriptome profiles of PD1+CD8+ T cells by uniform manifold approximation and projection (UMAP) analysis of high-parametric flow-cytometry data, dissecting the CD8+PD1+ and CD8+PD1− subsets (Fig.
To investigate the mechanisms that drive the increased NASH–HCC transition in the preventive anti-PD1 treatment-setting, we treated NASH-affected mice with combinations of treatments. Both anti-CD8–anti-PD1 and anti-TNF–anti-PD1 antibody treatments ameliorated liver damage, liver pathology and liver inflammation (Fig.
We next investigated CD8+ T cells from healthy or NAFLD/NASH-affected livers. In two independent cohorts of patients with NASH, we found enrichment of hepatic CD8+PD1+ T cells with a residency phenotype (by flow cytometry and mass cytometry) (Fig.
Different stages of NASH severity are considered to herald the development of liver cancer
To explore the concept of disrupted immune surveillance in NASH after anti-PD1 or anti-PDL1 treatment, we conducted a meta-analysis of three large randomized controlled phase III trials of immunotherapies in patients with advanced HCC (CheckMate-459
To specifically characterize the effect of anti-PD(L)1 immunotherapy with respect to underlying liver disease, we investigated a cohort of 130 patients with HCC (patients with NAFLD
Liver cancer develops primarily on the basis of chronic inflammation. The latter can be activated by immunotherapy to induce tumour regression in a subset of patients with liver cancer. However, the identification of patients who will respond to immunotherapy for HCC remains difficult. Our data identify a non-viral aetiology of liver damage and cancer (that is, NASH) as a predictor of unfavourable outcome in patients treated with immune-checkpoint inhibitors. The better response to immunotherapy in patients with virus-induced HCC than in patients with non-viral HCC might be due to the amount or quality of viral antigens or to a different liver micro-environment, possibly one that does not impair immune surveillance. These results might also have implications for patients with obesity and NALFD or NASH who have cancer at other organ sites (for example, melanoma, colon carcinoma, or breast cancer) and are at risk for liver damage and the development of liver cancer in response to systemically applied immunotherapy. Overall, our results provide comprehensive mechanistic insight and a rational basis for the stratification of patients with HCC according to their aetiology of liver damage and cancer for the design of future trials of personalized cancer therapy.
Standard mouse diet feeding (ad libitum water and food access) and treatment regimens were as described previously
Intraperitoneal glucose tolerance test and measurement of serum parameters were as described previously
MRI was done in the small animal imaging core facility in DKFZ using a Bruker BioSpec 9.4 Tesla (Ettlingen). Mice were anaesthetized with 3.5% sevoflurane, and imaged with T2-weighted imaging using a T2_TurboRARE sequence: TE = 22 ms, TR = 2,200 ms, field of view (FOV) 35 × 35 mm, slice thickness 1 mm, averages = 6, scan time 3 min 18 s, echo spacing 11 ms, rare factor 8, slices 20, image size 192 × 192 pixels, resolution 0.182 × 0.182 mm.
Liver homogenates were prepared as for western blotting
After perfusion and mechanical dissection, livers were incubated for up to 35 min at 37 °C with collagen IV (60 U final concentration (f.c.)) and DNase I (25 μg/ml f.c.), filtered at 100 μm, and washed with RPMI1640 (11875093, Thermo Fisher). Next, samples underwent a two-step Percoll gradient (25%/50% Percoll/HBSS) and centrifugation for 15 min at 1,800
For UMAP and FlowSOM plots, BD FACSymphony data (mouse and human) were exported from FlowJo (v10). Analyses were performed as described elsewhere
Single-cell capturing for scRNA-seq and library preparation were done as described previously
Velocyto (0.6) was used to estimate the spliced and unspliced counts from the pre-aligned bam files
After FACS purification, cells were resuspended in 50% (vol/vol) 2,2,2-trifluoroethanol in PBS pH 7.4 buffer and lysed by repeated sonication and freeze–thaw cycles. Proteins were denatured at 60 °C for 2 h, reduced using dithiothreitol at a final concentration of 5 mM (30 min at 60 °C), cooled to room temperature, alkylated using iodoacetamide at 25 mM (30 min at room temperature in the dark), and diluted 1:5 using 100 mM ammonium bicarbonate, pH 8.0. Proteins were digested overnight by trypsin (1:100 ratio, 37 °C), desalted using C18-based stage-tips, dried under vacuum, resuspended in 20 μl HPLC-grade water with 0.1% formic acid, and measured using A380.
We used 0.5 μg of peptides for proteomic analysis on a C18 column using a nano liquid chromatography system (EASY-nLC 1200, Thermo Fisher Scientific). Peptides were eluted using a gradient of 5–30% buffer B (80% acetonitrile and 0.1% formic acid) at a flow rate of 300 nl/min at a column temperature of 55 °C. Data were acquired by data-dependent Top15 acquisition using a high-resolution orbitrap tandem mass spectrometer (QExactive HFX, Thermo Scientific). All MS1 scans were acquired at 60,000 resolution with AGC target of 3 × 106, and MS2 scans were acquired at 15,000 resolution with AGC target of 1 × 105 and maximum injection time of 28 ms. Analyses were performed using MaxQuant (1.6.7.0), mouse UniProt Isoform fasta (Version: 2019-02-21, number of sequences 25,233) as a source for protein sequences. One per cent FDR was used for controlling at the peptide and protein levels, with a minimum of two peptides needed for consideration of analysis. GSEA was performed using ClusterProfiler (3.18)
Histology, immunohistochemistry, scanning, and automated analysis have been described previously
RNA isolation
Stimulation of CD8 T cells was as described elsewhere
Analysis of patient material (Supplementary Table
Flow cytometry of human samples (Extended Data Fig.
As previously reported, RNA-seq analysis was performed using the data from 206 snap-frozen biopsy samples from 206 patients diagnosed with NAFLD in France, Germany, Italy, and the UK and enrolled in the European NAFLD Registry (GEO accession GSE135251)
Sixty-five human FFPE biopsies from patients with NAFLD were included (Supplementary Table
Analyses used liver samples from patients undergoing bariatric surgery at the Department of Surgery at Heidelberg University Hospital (S-629/2013). Samples were preserved by FFPE for pathological evaluation and single cells were generated by mincing, using the Miltenyi tumour dissociation kit (130-095-929) per the manufacturer’s instructions, filtering through a 70-μm cell strainer and washing. ACK lysis using the respective buffer (Thermo Fischer Scientific A1049201) was performed, and samples were stored in FBS with 20% DMSO until further processing (scRNA-seq analysis and mass cytometry).
Cells were thawed in a 37 °C water bath, washed with PBS + 0.05 mM EDTA (10 min, 300
Antibody conjugates for mass cytometry were purchased from Fluidigm, generated in-house using antibody labelling kits (Fluidigm X8, MCP9), or as described before
Four healthy samples, 16 samples from patients with NASH cases, and non-tumoral tissue adjacent to HCC tumours from patients of the following aetiologies were selected: NASH (
The literature search was done through MEDLINE on PubMed, Cochrane Library, Web of Science, and clinicaltrials.gov, using the following searches: ‘checkpoint inhibitors’, ‘HCC’, ‘phase III’, between January 2010 and January 2020, and complemented by manual searches of conference abstracts and presentations. Single-centre, non-controlled trials, studies with insufficient data to extract HRs or 95% confidence intervals, and trials including disease entities other than HCC were excluded. As conference abstracts were not excluded, quality assessment of the included studies was not performed. Three studies
The retrospective analysis was approved by local Ethics Committees. Data from this cohort were published previously
A multi-institutional dataset that included 427 patients with HCC treated with immune checkpoint inhibitors between 2017 and 2019 in 11 tertiary-care referral centres specialized in the treatment of HCC was analysed. Clinical outcomes of this patient cohort have been reported elsewhere
No statistical methods were used to predetermine sample size. The experiments were not randomized and the investigators were not blinded to allocation during experiments and outcome assessment. Data were collected in Microsoft Excel. Mouse data are presented as the mean ± s.e.m. Pilot experiments and previously published results were used to estimate the sample size, such that appropriate statistical tests could yield significant results. Statistical analysis was performed using GraphPad Prism software version 7.03 (GraphPad Software). Exact
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Further information on research design is available in the
Any methods, additional references, Nature Research reporting summaries, source data, Extended Data Fig., supplementary information, acknowledgements, peer review information; details of author contributions and competing interests; and statements of data and code availability are available at
We thank the Newcastle Molecular Pathology Node Proximity Laboratory for their technical support; P. Sinn for technical support; the DKFZ FACS core facility for support for sorting; G. Tiegs and K. Neumann for support with
Design of the study: D.P., M.H. Performed breeding and housing of mice: D.P., E.K., V.L. Performed flow cytometry experiments: D.P., N.G.N., M.S., E.F., K. Stirm, J.K., M.D., E.K. Histological staining and analyses: D.P., M.S., D.H., F. Müller, V.L. Bulk or scRNA-seq: R.G., M.Q., A.D., A. Weiner, T.E., S.Y., I.S. Proteome analyses: A. Sinha. Meta-analyses: R.P. Human cohorts: O.G., M.P., D.L, D.I., F.C., C.M., D.T., Z.M.J. Immunotherapy treatment of genetic model of HCC: K.W., M.R.d.G., A.T., K.U. Designed/performed the clinical case study, provided tissue samples or mouse strains and/or scientific input: A.M., P.K.H., P.B., S.C., R.Y., M.V., F. Marra, J.M.S., M.A., E.B., V.R., T.P., A.D’A., N.P., L.R., A.K.D., B.S., K.P., M.M.K., A.V., M.P.-R., F.H., F.F., O.W., J.T., K. Schulze, H.W., S.K., H.K., D.G.D., F.K., D.-T.W., M.P.E., A. Weinmann, M. Bueter, F.R., A. Siebenhüner, S.D.D., J.-P.M., V.U., M.J., T.L., A. Schietinger, P.S., H.G.A., A.B., B.M.-S., L.Z., H.E.M., A.R.S., M.R., N.M., S.S., M. Bitzer, A.L., N.R., J.-F.D., T.U.M., A.K., M.K., Y.-H.H., N.D., A. Weber, P.N.M., D.J.P., T.D., R.R., J.C.M., F. Meissner, S.R., M.C., Q.M.A., I.A., P.K., B.B., J.M.L., M.H. All authors analysed data. D.P., P.K., J.M.L. and M.H. wrote the manuscript, and all authors contributed to writing and provided feedback.
Open access funding provided by Deutsches Krebsforschungszentrum (DKFZ) (1052).
The proteomics data described in this article are available at the PRIDE database, under the identifier PXD017236 or through the dataset website (
M.P. is an investigator for Bayer, BMS, Lilly, and Roche; has received speaker honoraria from Bayer, BMS, Eisai, Lilly, and MSD; is a consultant for Bayer, BMS, Eisai, Ipsen, Lilly, MSD, and Roche; and has received travel support from Bayer and BMS. D.P. works currently for Novo Nordisk. M. Szydlowksa works currently for Astra Zeneca. M.K. received honoraria from BMS as consultant and is an investigator for AstraZeneca and BMS. A.V. has served as consultant for Roche, Bayer, Lilly, BMS, Eisai, and Ipsen; has received speaking fees form Roche, Bayer, Lilly, BMS, Eisai, and Ipsen; and is an investigator for Roche, Bayer, Lilly, BMS, Eisai, and Ipsen. F.H. has received travel support from Bayer, Abbvie, and Gilead. M.P.-R. is an advisor/consultant for Astra Zeneca, Bayer, BMS, Eisai, Ipsen, Lilly, and MSD; has served as a speaker for Bayer, Eisai, and Lilly; and is an investigator for Bayer, BMS, Exelixis, and Lilly. F.F. has received travel support from Abbvie and Novartis. O.W. has served as consultant for Amgen, Bayer, BMS, Celgene, Eisai, Merck, Novartis, Roche, Servier, and Shire; has served as a speaker for Abbvie, Bayer, BMS, Celgene, Falk, Ipsen, Novartis, Roche, and Shire; and has received travel support from Abbvie, BMS, Ipsen, Novartis, and Servier. J.T. has served as consultant for Amgen, Bayer, BMS, Eisai, Lilly, Merck Serono, MSD, Ipsen, and Roche; has received travel support from BMS and Ipsen; has received speaking fees from Amgen, Bayer, BMS, Eisai, Lilly, Merck Serono, MSD, Ipsen, and Roche; and is an investigator for Amgen, Bayer, BMS, Eisai, Lilly, Merck Serono, MSD, Ipsen, and Roche. K.S. has served as consultant for Ipsen and Bayer; and conducts studies for Bayer, Roche, Lilly, MSD, and BMS. H.W. has served as speaker for Bayer, Eisai, and Ipsen; has served as a consultant for Bayer, Eisai, Lilly, BMS, Roche, and Ipsen; and conducts studies for Bayer, Roche, Lilly, MSD, and BMS. A. Weberis an advisor for BMS, Wako, Eisai, Roche, and Amgen. J.C.M. has received consulting honoraria from Abbvie, Bayer, BMS, Eisai, Gilead, Incyte, Intercept and MSD for work performed outside the current study. J.M.L. is receiving research support from Bayer HealthCare Pharmaceuticals, Eisai Inc, Bristol-Myers Squibb, Boehringer-Ingelheim and Ipsen, and consulting fees from Eli Lilly, Bayer HealthCare Pharmaceuticals, Bristol-Myers Squibb, Eisai Inc, Celsion Corporation, Exelixis, Merck, Ipsen, Genentech, Roche, Glycotest, Leerink Swann LLC, Fortress Biotech, Nucleix, Can-Fite Biopharma, Sirtex, Mina Alpha Ltd and AstraZeneca. J.M.S. serves as a consultant for Intercept Pharmaceuticals, Genfit, Gilead Sciences, BMS, Madrigal, Novartis, Pfizer, Roche, and Siemens-Healthineers; and has received research funding from Gilead Sciences. D.J.P. has received lecture fees from ViiV Healthcare and Bayer Healthcare; travel expenses from BMS and Bayer Healthcare; consulting fees from Mina Therapeutics, EISAI, Roche and Astra Zeneca; and research funding (to institution) from MSD and BMS. J.-F.D. has served on advisory committees for Abbvie, Bayer, Bristol-Myers Squibb, Falk, Genfit, Genkyotex, Gilead Sciences, HepaRegenix, Intercept, Lilly, Merck, and Novartis; and has spoken or taught at Bayer, Bristol-Myers Squibb, Intercept, Genfit, Gilead Sciences, Novartis, and Roche. L.R. has received consulting fees from Amgen, ArQule, Astra Zeneca, Basilea, Bayer, Celgene, Eisai, Exelixis, Hengrui, Incyte, Ipsen, Lilly, MSD, Nerviano Medical Sciences, Roche, and Sanofi; lectures fees from AbbVie, Amgen, Eisai, Gilead, Incyte, Ipsen, Lilly, Roche, Sanofi; travel expenses from Ipsen; and institutional research funding from Agios, ARMO BioSciences, AstraZeneca, BeiGene, Eisai, Exelixis, Fibrogen, Incyte, Ipsen, Lilly, MSD, and Roche. N.P. has received consulting fees from Amgen, Merck Serono, and Servier; lectures fees from AbbVie, Gilead and Lilly; travel expenses from Amgen and ArQule; and institutional research funding from Basilea, Merck Serono and Servier. T.P. has received institutional research funding from Lilly. D.G.D. has received consultant fees from Bayer, Simcere, Surface Oncology and BMS; and research grants from Bayer, Exelixis and BMS. The remaining authors declare no competing interests.
This file contains Supplementary Tables 1-15. Reporting Summary Gating strategy flow cytometry. Peer Review File
Source Data Fig. 1 Source Data Fig. 2 Source Data Fig. 3 Source Data Fig. 4 Source Data Extended Data Fig. 1 Source Data Extended Data Fig. 2 Source Data Extended Data Fig. 3 Source Data Extended Data Fig. 4 Source Data Extended Data Fig. 5 Source Data Extended Data Fig. 6 Source Data Extended Data Fig. 7 Source Data Extended Data Fig. 8 Source Data Extended Data Fig. 9 Source Data Extended Data Fig. 10
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