Immune Checkpoint Inhibition is Safe and Effective for Liver Cancer Prevention in a Mouse Model of Hepatocellular Carcinoma
Open accessPublished version of a preprint
Abstract
Cirrhosis is a high-risk state for hepatocellular carcinoma (HCC) development and represents an opportunity to prevent cancer. In the precancerous state of cirrhosis, there is an accumulation of neoantigens that may be specifically targetable through immunotherapy. We asked whether immune checkpoint inhibition could prevent tumorigenesis in a mouse model of diethylnitrosamine and carbon tetrachloride–induced HCC. We found that initiation of anti-PD-1 therapy prior to tumorigenesis could prevent up to 46% of liver tumors. This significant reduction in tumor burden was accompanied by infiltration of CD4+ Th cells and CD8+ cytotoxic T cells into the liver parenchyma. Importantly, anti-PD-1 therapy did not exacerbate liver dysfunction or worsen overall health in this liver disease model. Given the safety and preservation of quality of life observed with long-term immunotherapy use, an immunotherapy chemoprevention strategy is likely associated with a low risk-to-benefit ratio and high value care in select patients. These results encourage a prevention trial in cirrhotic patients with the highest risk of developing HCC. See related Spotlight by Mohammed et al., p. 897
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Claims other papers make about this one
Each is checked against this paper’s own text: does it say what the citing paper says it does?
Many murine liver cancer models enforce oncogene or tumor suppressor perturbations in all hepatocytes or use mutagens that humans are rarely exposed to.
“Because many murine liver cancer models enforce oncogene or tumor suppressor perturbations in all hepatocytes or use mutagens that humans are rarely exposed to (20, 21), one cannot solely depend on these models to identify the cell of origin or to track the evolution of HCCs.”
From The origin of hepatocellular carcinoma depends on metabolic zonation (2026) · cites this paper as [21]
The Discussion (page not available) says that commonly used mouse models rely on tumor-suppressor deletion or oncogene activation, while the Results describe a model using DEN to induce mutagenesis. The paper does not say that the genetic perturbations occur in all hepatocytes or that humans are rarely exposed to the mutagens used. (LLM, conf=0.84)
Experimental — accuracy not yet measured
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Tumor and Microenvironment Evolution during Immunotherapy with Nivolumab
Riaz, N., Havel, J. J., Makarov, V., Desrichard, A., Urba, W. J., Sims, J. S., Hodi, F. S., Martín-Algarra, S., Mandal, R., Sharfman, W. H., et al. (2017) · Cell · doi:10.1016/j.cell.2017.09.028 · Cited by 2,401 · see citing works
Ruiz de Galarreta, M., Bresnahan, E., Molina-Sánchez, P., Lindblad, K. E., Maier, B., Sia, D., Puigvehi, M., Miguela, V., Casanova-Acebes, M., Dhainaut, M., et al. (2019) · Cancer Discovery · doi:10.1158/2159-8290.cd-19-0074 · Cited by 912 · see citing works
The Polyploid State Plays a Tumor-Suppressive Role in the Liver
Zhang, S., Zhou, K., Luo, X., Li, L., Tu, H.-C., Sehgal, A., Nguyen, L. H., Zhang, Y., Gopal, P., Tarlow, B. D., et al. (2018) · Developmental Cell · doi:10.1016/j.devcel.2018.01.010 · Cited by 180 · cited 2× · see citing works
“Previous work from our laboratory and others has characterized the mutational landscape in DEN-induced tumors (45, 46), and our studies found recurrent mutations in several oncogenes and tumor suppressors (46).”
BackgroundSelf-citation
Flagged first, then by citations
Research works printed but not linked: 67
- [1]Llovet JM, Zucman-Rossi J, Pikarsky E, Sangro B, Schwartz M, Sherman M, Hepatocellular carcinoma. Nat Rev Dis Primers. 2016; 2: 16018. 27158749
- [2]Villanueva A Hepatocellular Carcinoma. N Engl J Med. 2019; 380: 1450– 62. 30970190
- [3]Hoshida Y, Villanueva A, Sangiovanni A, Sole M, Hur C, Andersson KL, Prognostic gene expression signature for patients with hepatitis C-related early-stage cirrhosis. Gastroenterology. 2013; 144: 1024– 30. 23333348 PMC3633736
- [4]Nakagawa S, Wei L, Song WM, Higashi T, Ghoshal S, Kim RS, Molecular liver cancer prevention in cirrhosis by organ transcriptome analysis and lysophosphatidic acid pathway inhibition. Cancer Cell. 2016; 30: 879– 90. 27960085 PMC5161110
- [5]Ioannou GN, Green PK, Beste LA, Mun EJ, Kerr KF, Berry K. Development of models estimating the risk of hepatocellular carcinoma after antiviral treatment for hepatitis C. J Hepatol. 2018; 69: 1088– 98. 30138686 PMC6201746
- [6]Ioannou GN, Green P, Kerr KF, Berry K. Models estimating risk of hepatocellular carcinoma in patients with alcohol or NAFLD-related cirrhosis for risk stratification. J Hepatol. 2019; 71: 523– 33. 31145929 PMC6702126
- [7]Singh S, Singh PP, Roberts LR, Sanchez W. Chemopreventive strategies in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol. 2014; 11: 45– 54. 23938452 PMC4334449
- [8]El-Khoueiry AB, Sangro B, Yau T, Crocenzi TS, Kudo M, Hsu C, Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet. 2017; 389: 2492– 502. 28434648 PMC7539326
- [9]Motzer RJ, Tannir NM, McDermott DF, Arén Frontera O, Melichar B, Choueiri TK, Nivolumab plus Ipilimumab versus Sunitinib in Advanced Renal-Cell Carcinoma. N Engl J Med. 2018; 378: 1277– 90. 29562145 PMC5972549
- [10]Brahmer J, Reckamp KL, Baas P, Crinò L, Eberhardt WE, Poddubskaya E, Nivolumab versus Docetaxel in Advanced Squamous-Cell Non-Small-Cell Lung Cancer. N Engl J Med. 2015; 373: 123– 35. 26028407 PMC4681400
- [11]Bellmunt J, de Wit R, Vaughn DJ, Fradet Y, Lee J-L, Fong L, Pembrolizumab as Second-Line Therapy for Advanced Urothelial Carcinoma. N Engl J Med. 2017; 376: 1015– 26. 28212060 PMC5635424
- [12]Robert C, Long GV, Brady B, Dutriaux C, Maio M, Mortier L, Nivolumab in previously untreated melanoma without BRAF mutation. N Engl J Med. 2015; 372: 320– 30. 25399552
- [13]Zhu AX, Finn RS, Edeline J, Cattan S, Ogasawara S, Palmer D, Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 2018; 19: 940– 52. 29875066
- [14]Topalian SL, Hodi FS, Brahmer JR, Gettinger SN, Smith DC, McDermott DF, Five-Year Survival and Correlates Among Patients With Advanced Melanoma, Renal Cell Carcinoma, or Non-Small Cell Lung Cancer Treated With Nivolumab. JAMA Oncol. 2019; 5(10): 1411– 1420. 31343665 PMC6659167
- [15]Schumacher TN, Schreiber RD. Neoantigens in cancer immunotherapy. Science. 2015; 348: 69– 74. 25838375
- [16]Yarchoan M, Johnson BA, Lutz ER, Laheru DA, Jaffee EM. Targeting neoantigens to augment antitumour immunity. Nat Rev Cancer. 2017; 17: 569. 28835723
- [17]Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015; 160: 48– 61. 25594174 PMC4856474
- [18]Van den Eynden J, Jiménez-Sánchez A, Miller ML, Larsson E. Lack of detectable neoantigen depletion signals in the untreated cancer genome. Nat Genet. 2019; 51: 1741– 8. 31768072 PMC6887557
- [19]Jiang T, Shi T, Zhang H, Hu J, Song Y, Wei J, Tumor neoantigens: from basic research to clinical applications. J Hematol Oncol. 2019; 12: 93. 31492199 PMC6731555
- [21]Rizvi NA, Hellmann MD, Snyder A, Kvistborg P, Makarov V, Havel JJ, Cancer immunology. Mutational landscape determines sensitivity to PD-1 blockade in non-small cell lung cancer. Science. 2015; 348: 124– 8. 25765070 PMC4993154
- [22]Zaretsky JM, Garcia-Diaz A, Shin DS, Escuin-Ordinas H, Hugo W, Hu-Lieskovan S, Mutations Associated with Acquired Resistance to PD-1 Blockade in Melanoma. N Engl J Med. 2016; 375: 819– 29. 27433843 PMC5007206
- [23]McGranahan N, Furness AJS, Rosenthal R, Ramskov S, Lyngaa R, Saini SK, Clonal neoantigens elicit T cell immunoreactivity and sensitivity to immune checkpoint blockade. Science. 2016; 351: 1463– 9. 26940869 PMC4984254
- [24]Zhu M, Lu T, Jia Y, Luo X, Gopal P, Li L, Somatic mutations increase hepatic clonal fitness and regeneration in chronic liver disease. Cell. 2019; 177: 608– 621.e12. 30955891 PMC6519461
- [25]Brunner SF, Roberts ND, Wylie LA, Moore L, Aitken SJ, Davies SE, Somatic mutations and clonal dynamics in healthy and cirrhotic human liver. Nature. 2019; 574: 538– 42. 31645727 PMC6837891
- [26]Cancer Genome Atlas Research Network. Electronic address: wheeler@bcm.edu, Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell. 2017; 169: 1327– 1341.e23. 28622513 PMC5680778
- [27]Li H, Durbin R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics. 2009; 25: 1754– 60. 19451168 PMC2705234
- [28]Cibulskis K, Lawrence MS, Carter SL, Sivachenko A, Jaffe D, Sougnez C, Sensitive detection of somatic point mutations in impure and heterogeneous cancer samples. Nat Biotechnol. 2013; 31: 213– 9. 23396013 PMC3833702
- [29]Koboldt DC, Zhang Q, Larson DE, Shen D, McLellan MD, Lin L, VarScan 2: somatic mutation and copy number alteration discovery in cancer by exome sequencing. Genome Res. 2012; 22: 568– 76. 22300766 PMC3290792
- [30]Hansen NF, Gartner JJ, Mei L, Samuels Y, Mullikin JC. Shimmer: detection of genetic alterations in tumors using next-generation sequence data. Bioinformatics. 2013; 29: 1498– 503. 23620360 PMC3673219
- [31]Chiang C, Layer RM, Faust GG, Lindberg MR, Rose DB, Garrison EP, SpeedSeq: ultra-fast personal genome analysis and interpretation. Nat Methods. 2015; 12: 966– 8. 26258291 PMC4589466
- [32]Chen X, Schulz-Trieglaff O, Shaw R, Barnes B, Schlesinger F, Källberg M, Manta: rapid detection of structural variants and indels for germline and cancer sequencing applications. Bioinformatics. 2016; 32: 1220– 2. 26647377
- [33]Saunders CT, Wong WSW, Swamy S, Becq J, Murray LJ, Cheetham RK. Strelka: accurate somatic small-variant calling from sequenced tumor-normal sample pairs. Bioinformatics. 2012; 28: 1811– 7. 22581179
- [34]Wang K, Li M, Hakonarson H. ANNOVAR: functional annotation of genetic variants from high-throughput sequencing data. Nucleic Acids Res. 2010; 38: e164. 20601685 PMC2938201
- [35]Lu T, Wang S, Xu L, Zhou Q, Singla N, Gao J, Tumor neoantigenicity assessment with CSiN score incorporates clonality and immunogenicity to predict immunotherapy outcomes. Sci Immunol. 2020; 5(44): eaaz3199. 32086382 PMC7239327
- [36]Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, STAR: ultrafast universal RNA-seq aligner. Bioinformatics. 2013; 29: 15– 21. 23104886 PMC3530905
- [37]Liao Y, Smyth GK, Shi W. featureCounts: an efficient general purpose program for assigning sequence reads to genomic features. Bioinformatics. 2014; 30: 923– 30. 24227677
- [38]Diwan BA, Rice JM, Ohshima M, Ward JM. Interstrain differences in susceptibility to liver carcinogenesis initiated by N-nitrosodiethylamine and its promotion by phenobarbital in C57BL/6NCr, C3H/HeNCrMTV- and DBA/2NCr mice. Carcinogenesis. 1986; 7: 215– 20. 3948311
- [39]Sorrelle N, Ganguly D, Dominguez ATA, Zhang Y, Huang H, Dahal LN, Improved Multiplex Immunohistochemistry for Immune Microenvironment Evaluation of Mouse Formalin-Fixed, Paraffin-Embedded Tissues. J Immunol. 2019; 202: 292– 9. 30510069 PMC6310091
- [40]Toyoda H, Tada T, Yasuda S, Mizuno K, Ito T, Kumada T. Dynamic evaluation of liver fibrosis to assess the risk of hepatocellular carcinoma in patients with chronic hepatitis C who achieved sustained virologic response. Clin Infect Dis. 2020; 70: 1208– 14. 31056696
- [41]Na SK, Song B-C. Development and surveillance of hepatocellular carcinoma in patients with sustained virologic response after antiviral therapy for chronic hepatitis C. Clin Mol Hepatol. 2019; 25: 234– 44. 30661334 PMC6759435
- [42]Heindryckx F, Colle I, Van Vlierberghe H. Experimental mouse models for hepatocellular carcinoma research. Int J Exp Pathol. 2009; 90: 367– 86. 19659896 PMC2741148
- [43]Verna L, Whysner J, Williams GM. N-nitrosodiethylamine mechanistic data and risk assessment: bioactivation, DNA-adduct formation, mutagenicity, and tumor initiation. Pharmacol Ther. 1996; 71: 57– 81. 8910949
- [44]Chalmers ZR, Connelly CF, Fabrizio D, Gay L, Ali SM, Ennis R, Analysis of 100,000 human cancer genomes reveals the landscape of tumor mutational burden. Genome Med. 2017; 9: 34. 28420421 PMC5395719
- [45]Connor F, Rayner TF, Aitken SJ, Feig C, Lukk M, Santoyo-Lopez J, Mutational landscape of a chemically-induced mouse model of liver cancer. J Hepatol. 2018; 69: 840– 50. 29958939 PMC6142872
- [47]Yanguas SC, Cogliati B, Willebrords J, Maes M, Colle I, van den Bossche B, Experimental models of liver fibrosis. Arch Toxicol. 2016; 90: 1025– 48. 26047667 PMC4705434
- [48]Basu S Carbon tetrachloride-induced lipid peroxidation: eicosanoid formation and their regulation by antioxidant nutrients. Toxicology. 2003; 189: 113– 27. 12821287
- [49]Weber LWD, Boll M, Stampfl A. Hepatotoxicity and mechanism of action of haloalkanes: carbon tetrachloride as a toxicological model. Crit Rev Toxicol. 2003; 33: 105– 36. 12708612
- [50]Francisco LM, Sage PT, Sharpe AH. The PD-1 pathway in tolerance and autoimmunity. Immunol Rev. 2010; 236: 219– 42. 20636820 PMC2919275
- [51]Pardoll DM. The blockade of immune checkpoints in cancer immunotherapy. Nat Rev Cancer. 2012; 12: 252– 64. 22437870 PMC4856023
- [52]Gentles AJ, Newman AM, Liu CL, Bratman SV, Feng W, Kim D, The prognostic landscape of genes and infiltrating immune cells across human cancers. Nat Med. 2015; 21: 938– 45. 26193342 PMC4852857
- [53]Kang T-W, Yevsa T, Woller N, Hoenicke L, Wuestefeld T, Dauch D, Senescence surveillance of pre-malignant hepatocytes limits liver cancer development. Nature. 2011; 479: 547– 51. 22080947
- [55]Kudo M, Finn RS, Qin S, Han KH, Ikeda K, Piscaglia F, Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. Lancet. 2018; 391: 1163– 73. 29433850
- [56]Bruix J, Qin S, Merle P, Granito A, Huang Y-H, Bodoky G, Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet. 2017; 389: 56– 66. 27932229
- [57]Abou-Alfa GK, Meyer T, Cheng AL, El-Khoueiry AB, Rimassa L, Ryoo BY, Cabozantinib in Patients with Advanced and Progressing Hepatocellular Carcinoma. N Engl J Med. 2018; 379: 54– 63. 29972759 PMC7523244
- [58]Zhu AX, Park JO, Ryoo B-Y, Yen C-J, Poon R, Pastorelli D, Ramucirumab versus placebo as second-line treatment in patients with advanced hepatocellular carcinoma following first-line therapy with sorafenib (REACH): a randomised, double-blind, multicentre, phase 3 trial. Lancet Oncol. 2015; 16: 859– 70. 26095784
- [59]Centanni M, Moes DJAR, Trocóniz IF, Ciccolini J, van Hasselt JGC. Clinical pharmacokinetics and pharmacodynamics of immune checkpoint inhibitors. Clin Pharmacokinet. 2019; 58: 835– 57. 30815848 PMC6584248
- [60]Huang AC, Orlowski RJ, Xu X, Mick R, George SM, Yan PK, A single dose of neoadjuvant PD-1 blockade predicts clinical outcomes in resectable melanoma. Nat Med. 2019; 25: 454– 61. 30804515 PMC6699626
- [61]Kanesvaran R, Cordoba R, Maggiore R. Immunotherapy in Older Adults With Advanced Cancers: Implications for Clinical Decision-Making and Future Research. Am Soc Clin Oncol Educ Book. 2018; 38: 400– 14. 30231397
- [62]Chernyak V, Fowler KJ, Kamaya A, Kielar AZ, Elsayes KM, Bashir MR, Liver Imaging Reporting and Data System (LI-RADS) Version 2018: Imaging of Hepatocellular Carcinoma in At-Risk Patients. Radiology. 2018; 289: 816– 30. 30251931 PMC6677371
- [63]Kanwal F, Singal AG. Surveillance for hepatocellular carcinoma: current best practice and future direction. Gastroenterology. 2019; 157: 54– 64. 30986389 PMC6636644
- [64]Uehara T, Pogribny IP, Rusyn I. The DEN and CCl4 -Induced Mouse Model of Fibrosis and Inflammation-Associated Hepatocellular Carcinoma. Curr Protoc Pharmacol. 2014; 66: 14.30.1– 10. 25181010 PMC4214366
- [65]Tolba R, Kraus T, Liedtke C, Schwarz M, Weiskirchen R. Diethylnitrosamine (DEN)-induced carcinogenic liver injury in mice. Lab Anim. 2015; 49: 59– 69. 25835739
- [66]Romualdo GR, Prata GB, da Silva TC, Fernandes AAH, Moreno FS, Cogliati B, Fibrosis-associated hepatocarcinogenesis revisited: Establishing standard medium-term chemically-induced male and female models. PLoS ONE. 2018; 13: e0203879. 30212575 PMC6136798
- [67]El-Serag HB. Hepatocellular carcinoma. N Engl J Med. 2011; 365: 1118– 27. 21992124
- [68]Tward AD, Jones KD, Yant S, Cheung ST, Fan ST, Chen X, Distinct pathways of genomic progression to benign and malignant tumors of the liver. Proc Natl Acad Sci USA. 2007; 104: 14771– 6. 17785413 PMC1964540
- [69]Chow EK-H, Fan L, Chen X, Bishop JM. Oncogene-specific formation of chemoresistant murine hepatic cancer stem cells. Hepatology. 2012; 56: 1331– 41. 22505225 PMC3418440
- [70]Chen X, Calvisi DF. Hydrodynamic transfection for generation of novel mouse models for liver cancer research. Am J Pathol. 2014; 184: 912– 23. 24480331 PMC3969989
Cited by
Sorted by citations
NASH limits anti-tumour surveillance in immunotherapy-treated HCC
Pfister, D., Núñez, N. G., Pinyol, R., Govaere, O., Pinter, M., Szydlowska, M., Gupta, R., Qiu, M., Deczkowska, A., Weiner, A., et al. (2021) · Nature · doi:10.1038/s41586-021-03362-0 · Cited by 1,294 · see citing works
The origin of hepatocellular carcinoma depends on metabolic zonation
Guo, J., Liang, R., Chung, A., Li, Z., Li, B., Chen, E., Li, L., Wang, J., Hsieh, M.-H., Fang, I. X., et al. (2026) · Science · doi:10.1126/science.adv7129 · Cited by 24 · see citing works
Self-citation
- Authors
- Chung, A. S., Mettlen, M., Ganguly, D., Lu, T., Wang, T., Brekken, R. A., Hsiehchen, D., & Zhu, H.
- Affiliations
- The University of Texas Southwestern Medical Center (US)
- Corresponding author
- Zhu, Hao
- ORCID
- Venue
- Cancer Prevention Research
- Published
- 2020-11-01
- Volume
- 13
- Issue
- 11
- Pages
- 911-922
- ISSN
- 1940-6207 (ISSN-L)
- 1940-6215
- PMC
- PMC8284815
- OpenAlex
- W3081025330
- Cited by
- 29
- References
- 3
- Citations by year
- 2026: 2 · 2025: 4 · 2024: 4 · 2023: 8 · 2022: 5 · 2021: 5 · 2020: 1
- FWCI
- 1.72
- Type
- journal article
- Language
- en
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Cancer Prevention Research (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Funders
- CPRIT (RP190208)
- Pollack Foundation
- NCI (R01 CA251928)
- Stand Up To Cancer Innovative Research grant (SU2C-AACR-IRG 10-16)
- Topics
- Health Sciences › Medicine › Oncology › Cancer Immunotherapy and Biomarkers
- Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
- Health Sciences › Medicine › Epidemiology › Hepatitis B Virus Studies
- Keywords
- immune checkpoint blockade, hepatocellular carcinoma, liver cancer prevention, liver cirrhosis, anti-PD-1 therapy, chemoprevention, diethylnitrosamine, carbon tetrachloride, tumor immunology, CD8+ cytotoxic T lymphocytes, CD4+ T cells, tumor burden, immunotherapy safety
- MeSH terms
- Immune Checkpoint Inhibitors, Animals, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Quality of Life, Mice, Programmed Cell Death 1 Receptor
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review