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Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma

A Publisher Correction to this article was published on 11 March 2020

This article has been updated


Hepatocellular carcinoma (HCC) is the most common form of primary adult liver cancer. After nearly a decade with sorafenib as the only approved treatment, multiple new agents have demonstrated efficacy in clinical trials, including the targeted therapies regorafenib, lenvatinib and cabozantinib, the anti-angiogenic antibody ramucirumab, and the immune checkpoint inhibitors nivolumab and pembrolizumab. Although these agents offer new promise to patients with HCC, the optimal choice and sequence of therapies remains unknown and without established biomarkers, and many patients do not respond to treatment. The advances and the decreasing costs of molecular measurement technologies enable profiling of HCC molecular features (such as genome, transcriptome, proteome and metabolome) at different levels, including bulk tissues, animal models and single cells. The release of such data sets to the public enhances the ability to search for information from these legacy studies and provides the opportunity to leverage them to understand HCC mechanisms, rationally develop new therapeutics and identify candidate biomarkers of treatment response. Here, we provide a comprehensive review of public data sets related to HCC and discuss how emerging artificial intelligence methods can be applied to identify new targets and drugs as well as to guide therapeutic choices for improved HCC treatment.

Key points

  • The past few years have witnessed the generation of big omics data across multiple modalities in hepatocellular carcinoma (HCC) — from primary to metastatic cancer, from bulk tissues to single cells and from patients to preclinical models.

  • Big data brings new hope but also new challenges in translating data points to therapeutics.

  • Multiple new targeted therapies have shown efficacy in HCC, yet the optimal choice and sequence of therapies for individual patients is unknown, without established clinical biomarkers of response or resistance.

  • A systems approach that aims to target a list of disease molecular features, such as gene expression signatures, can be used to complement the conventional target-based approach.

  • Big data analysis, including pan-cancer studies, might help quantify biological differences between preclinical models and patients, further guiding translational research, which is especially critical for understudied cancers such as HCC.

  • Emerging artificial intelligence methods, including deep learning, could empower big data in HCC therapeutic discovery and identification of predictive biomarkers.

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Fig. 1: Translational research and big data.
Fig. 2: Connecting genomic features and therapeutic targets in HCC.
Fig. 3: Translating big data to therapeutics.

Change history


  1. 1.

    Bray, F. et al. Global Cancer Statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J. Clin. 68, 394–424 (2018).

    PubMed  PubMed Central  Google Scholar 

  2. 2.

    Global Burden of Disease Liver Cancer Collaboration et al. The burden of primary liver cancer and underlying etiologies from 1990 to 2015 at the global, regional, and national level: results from the global burden of disease study 2015. JAMA Oncol. 3, 1683–1691 (2017).

    PubMed Central  Google Scholar 

  3. 3.

    Ryerson, A. B. et al. Annual report to the nation on the status of cancer, 1975-2012, featuring the increasing incidence of liver cancer. Cancer 122, 1312–1337 (2016).

    PubMed  Google Scholar 

  4. 4.

    American Cancer Society. Key statistics about liver cancer. American Cancer Society (2019).

  5. 5.

    Singal, A. G. & El-Serag, H. B. Hepatocellular carcinoma from epidemiology to prevention: translating knowledge into practice. Clin. Gastroenterol. Hepatol. 13, 2140–2151 (2015).

    PubMed  PubMed Central  Google Scholar 

  6. 6.

    de Lope, C. R., Tremosini, S., Forner, A., Reig, M. & Bruix, J. Management of HCC. J. Hepatol. 56 (Suppl. 1), S75–S87 (2012).

    Google Scholar 

  7. 7.

    Mazzaferro, V. et al. Liver transplantation for the treatment of small hepatocellular carcinomas in patients with cirrhosis. N. Engl. J. Med. 334, 693–699 (1996).

    CAS  PubMed  Google Scholar 

  8. 8.

    Llovet, J. M. et al. Arterial embolisation or chemoembolisation versus symptomatic treatment in patients with unresectable hepatocellular carcinoma: a randomised controlled trial. Lancet 359, 1734–1739 (2002).

    PubMed  Google Scholar 

  9. 9.

    Llovet, J. M. et al. Sorafenib in advanced hepatocellular carcinoma. N. Engl. J. Med. 359, 378–390 (2008).

    CAS  Google Scholar 

  10. 10.

    Cheng, A. L. et al. Efficacy and safety of sorafenib in patients in the Asia-Pacific region with advanced hepatocellular carcinoma: a phase III randomised, double-blind, placebo-controlled trial. Lancet. Oncol. 10, 25–34 (2009).

    CAS  PubMed  Google Scholar 

  11. 11.

    Kudo, M. et al. Lenvatinib versus sorafenib in first-line treatment of patients with unresectable hepatocellular carcinoma: a randomised phase 3 non-inferiority trial. Lancet 391, 1163–1173 (2018).

    CAS  PubMed  Google Scholar 

  12. 12.

    Bruix, J. et al. Regorafenib for patients with hepatocellular carcinoma who progressed on sorafenib treatment (RESORCE): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet 389, 56–66 (2017).

    CAS  PubMed  Google Scholar 

  13. 13.

    Abou-Alfa, G. K. et al. Cabozantinib in patients with advanced and progressing hepatocellular carcinoma. N. Engl. J. Med. 379, 54–63 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  14. 14.

    Zhu, A. X. et al. Ramucirumab after sorafenib in patients with advanced hepatocellular carcinoma and increased α-fetoprotein concentrations (REACH-2): a randomised, double-blind, placebo-controlled, phase 3 trial. Lancet Oncol. 20, 282–296 (2019).

    CAS  PubMed  Google Scholar 

  15. 15.

    El-Khoueiry, A. B. et al. Nivolumab in patients with advanced hepatocellular carcinoma (CheckMate 040): an open-label, non-comparative, phase 1/2 dose escalation and expansion trial. Lancet 389, 2492–2502 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  16. 16.

    Zhu, A. X. et al. Pembrolizumab in patients with advanced hepatocellular carcinoma previously treated with sorafenib (KEYNOTE-224): a non-randomised, open-label phase 2 trial. Lancet Oncol. 19, 940–952 (2018).

    PubMed  Google Scholar 

  17. 17.

    Okusaka, T. & Ikeda, M. Immunotherapy for hepatocellular carcinoma: current status and future perspectives. ESMO Open. 3 (Suppl. 1), e000455 (2018).

    Google Scholar 

  18. 18.

    US National Library of Medicine. (2019).

  19. 19.

    Chen, B. & Butte, A. J. Leveraging big data to transform target selection and drug discovery. Clin. Pharmacol. Ther. 99, 285–297 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  20. 20.

    Wooden, B., Goossens, N., Hoshida, Y. & Friedman, S. L. Using big data to discover diagnostics and therapeutics for gastrointestinal and liver diseases. Gastroenterology 152, 53–67.e3 (2017).

    PubMed  Google Scholar 

  21. 21.

    Llovet, J. M., Montal, R., Sia, D. & Finn, R. S. Molecular therapies and precision medicine for hepatocellular carcinoma. Nat. Rev. Clin. Oncol. 15, 599–616 (2018).

    PubMed  Google Scholar 

  22. 22.

    Cancer Genome Atlas Research Network. Comprehensive and integrative genomic characterization of hepatocellular carcinoma. Cell 169, 1327–1341.e23 (2017).

    Google Scholar 

  23. 23.

    Lin, C.-P., Liu, C.-R., Lee, C.-N., Chan, T.-S. & Liu, H. E. Targeting c-Myc as a novel approach for hepatocellular carcinoma. World J. Hepatol. 2, 16–20 (2010).

    PubMed  PubMed Central  Google Scholar 

  24. 24.

    Belmar, J. & Fesik, S. W. Small molecule Mcl-1 inhibitors for the treatment of cancer. Pharmacol. Ther. 145, 76–84 (2015).

    CAS  PubMed  Google Scholar 

  25. 25.

    US National Library of Medicine. (2019).

  26. 26.

    Stein, S. et al. Safety and clinical activity of 1L atezolizumab + bevacizumab in a phase Ib study in hepatocellular carcinoma (HCC). J. Clin. Oncol. 36 (Suppl. 15), 4074 (2018).

    Google Scholar 

  27. 27.

    Schulze, K. et al. Exome sequencing of hepatocellular carcinomas identifies new mutational signatures and potential therapeutic targets. Nat. Genet. 47, 505–511 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  28. 28.

    Ahn, S. M. et al. Genomic portrait of resectable hepatocellular carcinomas: implications of RB1 and FGF19 aberrations for patient stratification. Hepatology 60, 1972–1982 (2014).

    CAS  PubMed  Google Scholar 

  29. 29.

    Fujimoto, A. et al. Whole-genome mutational landscape and characterization of noncoding and structural mutations in liver cancer. Nat. Genet. 48, 500–509 (2016).

    CAS  PubMed  Google Scholar 

  30. 30.

    Guichard, C. et al. Integrated analysis of somatic mutations and focal copy-number changes identifies key genes and pathways in hepatocellular carcinoma. Nat. Genet. 44, 694–698 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  31. 31.

    Totoki, Y. et al. Trans-ancestry mutational landscape of hepatocellular carcinoma genomes. Nat. Genet. 46, 1267–1273 (2014).

    CAS  PubMed  Google Scholar 

  32. 32.

    Chaudhary, K. et al. Multimodal meta-analysis of 1,494 hepatocellular carcinoma samples reveals significant impact of consensus driver genes on phenotypes. Clin. Cancer Res. 25, 463–472 (2019).

    CAS  PubMed  Google Scholar 

  33. 33.

    Iizuka, N. et al. Differential gene expression in distinct virologic types of hepatocellular carcinoma: association with liver cirrhosis. Oncogene 22, 3007–3014 (2003).

    CAS  PubMed  Google Scholar 

  34. 34.

    Chen, X. et al. Gene expression patterns in human liver cancers. Mol. Biol. Cell 13, 1929–1939 (2002).

    CAS  PubMed  PubMed Central  Google Scholar 

  35. 35.

    Zhu, Z. W. et al. Enhanced glypican-3 expression differentiates the majority of hepatocellular carcinomas from benign hepatic disorders. Gut 48, 558–564 (2001).

    CAS  PubMed  PubMed Central  Google Scholar 

  36. 36.

    Jia, H.-L. et al. Gene expression profiling reveals potential biomarkers of human hepatocellular carcinoma. Clin. Cancer Res. 13, 1133–1139 (2007).

    CAS  PubMed  Google Scholar 

  37. 37.

    GTEx Consortium. The genotype-tissue expression (GTEx) project. Nat. Genet. 45, 580–585 (2013).

    Google Scholar 

  38. 38.

    Li, J. et al. TCPA: a resource for cancer functional proteomics data. Nat. Methods 10, 1046–1047 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  39. 39.

    Tsai, T.-H. et al. LC-MS/MS based serum proteomics for identification of candidate biomarkers for hepatocellular carcinoma. Proteomics 15, 2369–2381 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  40. 40.

    Jiang, Y. et al. Proteomics identifies new therapeutic targets of early-stage hepatocellular carcinoma. Nature 567, 257–261 (2019).

    CAS  Google Scholar 

  41. 41.

    Huang, Q. et al. Metabolic characterization of hepatocellular carcinoma using nontargeted tissue metabolomics. Cancer Res. 73, 4992–5002 (2013).

    CAS  PubMed  Google Scholar 

  42. 42.

    Di Poto, C. et al. Metabolomic characterization of hepatocellular carcinoma in patients with liver cirrhosis for biomarker discovery. Cancer Epidemiol. Biomarkers Prev. 26, 675–683 (2017).

    PubMed  Google Scholar 

  43. 43.

    Chen, T. et al. Serum and urine metabolite profiling reveals potential biomarkers of human hepatocellular carcinoma. Mol. Cell. Proteomics 10, M110.004945 (2011).

    PubMed  PubMed Central  Google Scholar 

  44. 44.

    Pfister, S. X. & Ashworth, A. Marked for death: targeting epigenetic changes in cancer. Nat. Rev. Drug Discov. 16, 241–263 (2017).

    CAS  PubMed  Google Scholar 

  45. 45.

    Revill, K. et al. Genome-wide methylation analysis and epigenetic unmasking identify tumor suppressor genes in hepatocellular carcinoma. Gastroenterology 145, 1424–1435.e1-25 (2013).

    CAS  PubMed  Google Scholar 

  46. 46.

    Robinson, D. R. et al. Integrative clinical genomics of metastatic cancer. Nature 548, 297–303 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  47. 47.

    Zheng, C. et al. Landscape of infiltrating T cells in liver cancer revealed by single-cell sequencing. Cell 169, 1342–1356.e16 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  48. 48.

    MacParland, S. A. et al. Single cell RNA sequencing of human liver reveals distinct intrahepatic macrophage populations. Nat. Commun. 9, 4383 (2018).

    PubMed  PubMed Central  Google Scholar 

  49. 49.

    Zheng, H. et al. Single-cell analysis reveals cancer stem cell heterogeneity in hepatocellular carcinoma. Hepatology 68, 127–140 (2018).

    PubMed  Google Scholar 

  50. 50.

    Hou, Y. et al. Single-cell triple omics sequencing reveals genetic, epigenetic, and transcriptomic heterogeneity in hepatocellular carcinomas. Cell Res. 26, 304–319 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  51. 51.

    Han, X. et al. Mapping the mouse cell atlas by Microwell-seq. Cell 172, 1091–1107.e17 (2018).

    CAS  PubMed  Google Scholar 

  52. 52.

    Tabula Muris Consortium et al. Single-cell transcriptomics of 20 mouse organs creates a Tabula Muris. Nature 562, 367–372 (2018).

    Google Scholar 

  53. 53.

    Regev, A. et al. The human cell atlas. eLife 6, e27041 (2017).

    PubMed  PubMed Central  Google Scholar 

  54. 54.

    Barretina, J. et al. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature 483, 603–607 (2012).

    CAS  PubMed  PubMed Central  Google Scholar 

  55. 55.

    Ghandi, M. et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature 569, 503–508 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  56. 56.

    Klijn, C. et al. A comprehensive transcriptional portrait of human cancer cell lines. Nat. Biotechnol. 33, 306–312 (2015).

    CAS  PubMed  Google Scholar 

  57. 57.

    Yang, W. et al. Genomics of drug sensitivity in cancer (GDSC): a resource for therapeutic biomarker discovery in cancer cells. Nucleic Acids Res. 41, D955–D961 (2013).

    CAS  PubMed  Google Scholar 

  58. 58.

    Li, J. et al. Characterization of human cancer cell lines by reverse-phase protein arrays. Cancer Cell 31, 225–239 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  59. 59.

    Qiu, Z. et al. A pharmacogenomic landscape in human liver cancers. Cancer Cell 36, 179–193.e11 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  60. 60.

    Broutier, L. et al. Human primary liver cancer-derived organoid cultures for disease modeling and drug screening. Nat. Med. 23, 1424–1435 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  61. 61.

    Chen, X. & Calvisi, D. F. Hydrodynamic transfection for generation of novel mouse models for liver cancer research. Am. J. Pathol. 184, 912–923 (2014).

    CAS  PubMed  PubMed Central  Google Scholar 

  62. 62.

    Ruiz de Galarreta, M. et al. β-catenin activation promotes immune escape and resistance to anti-PD-1 therapy in hepatocellular carcinoma. Cancer Discov. 9, 1124–1141 (2019).

    CAS  PubMed  Google Scholar 

  63. 63.

    Joshi, J. J. et al. H3B-6527 is a potent and selective inhibitor of FGFR4 in FGF19-driven hepatocellular carcinoma. Cancer Res. 77, 6999–7013 (2017).

    CAS  PubMed  Google Scholar 

  64. 64.

    Huynh, H. et al. Infigratinib mediates vascular normalization, impairs metastasis, and improves chemotherapy in hepatocellular carcinoma. Hepatology 69, 943–958 (2019).

    CAS  PubMed  Google Scholar 

  65. 65.

    Lee, J.-S. et al. Application of comparative functional genomics to identify best-fit mouse models to study human cancer. Nat. Genet. 36, 1306–1311 (2004).

    CAS  PubMed  Google Scholar 

  66. 66.

    Conte, N. et al. PDX Finder: a portal for patient-derived tumor xenograft model discovery. Nucleic Acids Res. 47, D1073–D1079 (2019).

    CAS  PubMed  Google Scholar 

  67. 67.

    Su, W.-H. et al. OncoDB.HCC: an integrated oncogenomic database of hepatocellular carcinoma revealed aberrant cancer target genes and loci. Nucleic Acids Res. 35, D727–D731 (2007).

    CAS  PubMed  Google Scholar 

  68. 68.

    He, S. et al. PDXliver: a database of liver cancer patient derived xenograft mouse models. BMC Cancer 18, 550 (2018).

    PubMed  PubMed Central  Google Scholar 

  69. 69.

    Cocco, E., Scaltriti, M. & Drilon, A. NTRK fusion-positive cancers and TRK inhibitor therapy. Nat. Rev. Clin. Oncol. 15, 731–747 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  70. 70.

    National Cancer Institute. Targeted Cancer Therapies Fact Sheet. NCI (2019).

  71. 71.

    Vilchez, V., Turcios, L., Marti, F. & Gedaly, R. Targeting Wnt/β-catenin pathway in hepatocellular carcinoma treatment. World J. Gastroenterol. 22, 823–832 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  72. 72.

    Meek, D. W. Regulation of the p53 response and its relationship to cancer. Biochem. J. 469, 325–346 (2015).

    CAS  PubMed  Google Scholar 

  73. 73.

    Toledo, F. & Wahl, G. M. MDM2 and MDM4: p53 regulators as targets in anticancer therapy. Int. J. Biochem. Cell Biol. 39, 1476–1482 (2007).

    CAS  PubMed  PubMed Central  Google Scholar 

  74. 74.

    Ruden, M. & Puri, N. Novel anticancer therapeutics targeting telomerase. Cancer Treat. Rev. 39, 444–456 (2013).

    CAS  PubMed  Google Scholar 

  75. 75.

    US National Library of Medicine. (2019).

  76. 76.

    Rubio-Perez, C. et al. In silico prescription of anticancer drugs to cohorts of 28 tumor types reveals targeting opportunities. Cancer Cell 27, 382–396 (2015).

    CAS  PubMed  Google Scholar 

  77. 77.

    Lin, D.-C. et al. Genomic and epigenomic heterogeneity of hepatocellular carcinoma. Cancer Res. 77, 2255–2265 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  78. 78.

    Thillai, K., Ross, P. & Sarker, D. Molecularly targeted therapy for advanced hepatocellular carcinoma - a drug development crisis? World J. Gastrointest. Oncol. 8, 173–185 (2016).

    PubMed  PubMed Central  Google Scholar 

  79. 79.

    Rimassa, L. et al. Tivantinib for second-line treatment of MET-high, advanced hepatocellular carcinoma (METIV-HCC): a final analysis of a phase 3, randomised, placebo-controlled study. Lancet Oncol. 19, 682–693 (2018).

    CAS  PubMed  Google Scholar 

  80. 80.

    US National Library of Medicine. (2019).

  81. 81.

    Liu, M. et al. Integrative epigenetic analysis reveals therapeutic targets to the DNA methyltransferase inhibitor guadecitabine (SGI-110) in hepatocellular carcinoma. Hepatology 68, 1412–1428 (2018).

    CAS  PubMed  Google Scholar 

  82. 82.

    US National Library of Medicine. (2019).

  83. 83.

    US National Library of Medicine. (2019).

  84. 84.

    Dudley, J. T. et al. Computational repositioning of the anticonvulsant topiramate for inflammatory bowel disease. Sci. Transl. Med. 3, 96ra76 (2011).

    CAS  PubMed  PubMed Central  Google Scholar 

  85. 85.

    Jahchan, N. S. et al. A drug repositioning approach identifies tricyclic antidepressants as inhibitors of small cell lung cancer and other neuroendocrine tumors. Cancer Discov. 3, 1364–1377 (2013).

    CAS  PubMed  Google Scholar 

  86. 86.

    Brum, A. M. et al. Connectivity Map-based discovery of parbendazole reveals targetable human osteogenic pathway. Proc. Natl Acad. Sci. USA 112, 12711–12716 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  87. 87.

    Lamb, J. et al. The Connectivity Map: using gene-expression signatures to connect small molecules, genes, and disease. Science 313, 1929–1935 (2006).

    CAS  PubMed  Google Scholar 

  88. 88.

    Pessetto, Z. Y. et al. In silico and in vitro drug screening identifies new therapeutic approaches for Ewing sarcoma. Oncotarget 8, 4079–4095 (2017).

    PubMed  Google Scholar 

  89. 89.

    Chen, B. et al. Computational discovery of niclosamide ethanolamine, a repurposed drug candidate that reduces growth of hepatocellular carcinoma cells in vitro and in mice by inhibiting cell division cycle 37 signaling. Gastroenterology 152, 2022–2036 (2017).

    CAS  PubMed  Google Scholar 

  90. 90.

    Chen, B. et al. Reversal of cancer gene expression correlates with drug efficacy and reveals therapeutic targets. Nat. Commun. 8, 16022 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  91. 91.

    Caicedo, J. C., Singh, S. & Carpenter, A. E. Applications in image-based profiling of perturbations. Curr. Opin. Biotechnol. 39, 134–142 (2016).

    CAS  PubMed  Google Scholar 

  92. 92.

    Duffy, A. G. et al. Tremelimumab in combination with ablation in patients with advanced hepatocellular carcinoma. J. Hepatol. 66, 545–551 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  93. 93.

    Robert, C. et al. Pembrolizumab versus ipilimumab in advanced melanoma. N. Engl. J. Med. 372, 2521–2532 (2015).

    CAS  PubMed  Google Scholar 

  94. 94.

    Borghaei, H. et al. Nivolumab versus docetaxel in advanced nonsquamous non-small-cell lung cancer. N. Engl. J. Med. 373, 1627–1639 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  95. 95.

    Thorsson, V. et al. The immune landscape of cancer. Immunity 48, 812–830.e14 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  96. 96.

    Varn, F. S., Wang, Y., Mullins, D. W., Fiering, S. & Cheng, C. Systematic pan-cancer analysis reveals immune cell interactions in the tumor microenvironment. Cancer Res. 77, 1271–1282 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  97. 97.

    Rooney, M. S., Shukla, S. A., Wu, C. J., Getz, G. & Hacohen, N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell 160, 48–61 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  98. 98.

    Sia, D. et al. Identification of an immune-specific class of hepatocellular carcinoma, based on molecular features. Gastroenterology 153, 812–826 (2017).

    CAS  PubMed  Google Scholar 

  99. 99.

    Rohr-Udilova, N. et al. Deviations of the immune cell landscape between healthy liver and hepatocellular carcinoma. Sci. Rep. 8, 6220 (2018).

    PubMed  PubMed Central  Google Scholar 

  100. 100.

    Grinberg-Bleyer, Y. et al. NF-κB c-Rel is crucial for the regulatory T cell immune checkpoint in cancer. Cell 170, 1096–1108.e13 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  101. 101.

    Lee, J.-S. et al. Classification and prediction of survival in hepatocellular carcinoma by gene expression profiling. Hepatology 40, 667–676 (2004).

    CAS  PubMed  Google Scholar 

  102. 102.

    Hoshida, Y. et al. Integrative transcriptome analysis reveals common molecular subclasses of human hepatocellular carcinoma. Cancer Res. 69, 7385–7392 (2009).

    CAS  PubMed  PubMed Central  Google Scholar 

  103. 103.

    Liu, G., Dong, C. & Liu, L. Integrated multiple “-omics” data reveal subtypes of hepatocellular carcinoma. PLOS ONE 11, e0165457 (2016).

    PubMed  PubMed Central  Google Scholar 

  104. 104.

    Chaudhary, K., Poirion, O. B., Lu, L. & Garmire, L. X. Deep learning-based multi-omics integration robustly predicts survival in liver cancer. Clin. Cancer Res. 24, 1248–1259 (2018).

    CAS  PubMed  Google Scholar 

  105. 105.

    Zucman-Rossi, J., Villanueva, A., Nault, J.-C. & Llovet, J. M. Genetic landscape and biomarkers of hepatocellular carcinoma. Gastroenterology 149, 1226–1239.e4 (2015).

    CAS  PubMed  Google Scholar 

  106. 106.

    Iorio, F. et al. A landscape of pharmacogenomic interactions in cancer. Cell 166, 740–754 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  107. 107.

    Seashore-Ludlow, B. et al. Harnessing connectivity in a large-scale small-molecule sensitivity dataset. Cancer Discov. 5, 1210–1223 (2015).

    CAS  PubMed  PubMed Central  Google Scholar 

  108. 108.

    Basu, A. et al. An interactive resource to identify cancer genetic and lineage dependencies targeted by small molecules. Cell 154, 1151–1161 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  109. 109.

    Zhu, A. X. et al. REACH-2: A randomized, double-blind, placebo-controlled phase 3 study of ramucirumab versus placebo as second-line treatment in patients with advanced hepatocellular carcinoma (HCC) and elevated baseline α-fetoprotein (AFP) following first-line sorafenib. J. Clin. Oncol. 36 (Suppl. 15), 4003 (2018).

    Google Scholar 

  110. 110.

    Hoshida, Y. et al. Molecular classification and novel targets in hepatocellular carcinoma: recent advancements. Semin. Liver Dis. 30, 35–51 (2010).

    CAS  PubMed  PubMed Central  Google Scholar 

  111. 111.

    Yoshihara, K. et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat. Commun. 4, 2612 (2013).

    PubMed  Google Scholar 

  112. 112.

    Chen, B., Sirota, M., Fan-Minogue, H., Hadley, D. & Butte, A. J. Relating hepatocellular carcinoma tumor samples and cell lines using gene expression data in translational research. BMC Med. Genomics 8 (Suppl. 2), S5 (2015).

    PubMed  PubMed Central  Google Scholar 

  113. 113.

    Uhlen, M. et al. A pathology atlas of the human cancer transcriptome. Science 357, eaan2507 (2017).

    Google Scholar 

  114. 114.

    Hirschfield, H. et al. In vitro modeling of hepatocellular carcinoma molecular subtypes for anti-cancer drug assessment. Exp. Mol. Med. 50, e419 (2018).

    PubMed  PubMed Central  Google Scholar 

  115. 115.

    Chen, H., Engkvist, O., Wang, Y., Olivecrona, M. & Blaschke, T. The rise of deep learning in drug discovery. Drug Discov. Today 23, 1241–1250 (2018).

    PubMed  Google Scholar 

  116. 116.

    Mamoshina, P., Vieira, A., Putin, E. & Zhavoronkov, A. Applications of deep learning in biomedicine. Mol. Pharmaceutics 13, 1445–1454 (2016).

    CAS  Google Scholar 

  117. 117.

    Ma, J., Sheridan, R. P., Liaw, A., Dahl, G. E. & Svetnik, V. Deep neural nets as a method for quantitative structure–activity relationships. J. Chem. Inf. Model. 55, 263–274 (2015).

    CAS  PubMed  Google Scholar 

  118. 118.

    Coley, C. W., Barzilay, R., Green, W. H., Jaakkola, T. S. & Jensen, K. F. Convolutional embedding of attributed molecular graphs for physical property prediction. J. Chem. Inf. Model. 57, 1757–1772 (2017).

    CAS  PubMed  Google Scholar 

  119. 119.

    Lusci, A., Pollastri, G. & Baldi, P. Deep architectures and deep learning in chemoinformatics: the prediction of aqueous solubility for drug-like molecules. J. Chem. Inf. Model. 53, 1563–1575 (2013).

    CAS  PubMed  PubMed Central  Google Scholar 

  120. 120.

    Aliper, A. et al. Deep learning applications for predicting pharmacological properties of drugs and drug repurposing using transcriptomic data. Mol. Pharm. 13, 2524–2530 (2016).

    CAS  PubMed  PubMed Central  Google Scholar 

  121. 121.

    Kadurin, A., Nikolenko, S., Khrabrov, K., Aliper, A. & Zhavoronkov, A. druGAN: an advanced generative adversarial autoencoder model for de novo generation of new molecules with desired molecular properties in silico. Mol. Pharmaceutics 14, 3098–3104 (2017).

    CAS  Google Scholar 

  122. 122.

    Merkwirth, C. & Lengauer, T. Automatic generation of complementary descriptors with molecular graph networks. J. Chem. Inf. Model. 45, 1159–1168 (2005).

    CAS  PubMed  Google Scholar 

  123. 123.

    Liu, B. et al. Retrosynthetic reaction prediction using neural sequence-to-sequence models. ACS Cent. Sci. 3, 1103–1113 (2017).

    CAS  PubMed  PubMed Central  Google Scholar 

  124. 124.

    Alakwaa, F. M., Chaudhary, K. & Garmire, L. X. Deep learning accurately predicts estrogen receptor status in breast cancer metabolomics data. J. Proteome Res. 17, 337–347 (2018).

    CAS  PubMed  Google Scholar 

  125. 125.

    Christiansen, E. M. et al. In silico labeling: predicting fluorescent labels in unlabeled images. Cell 173, 792–803.e19 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  126. 126.

    Zeng, W. Z. D., Glicksberg, B. S., Li, Y. & Chen, B. Selecting precise reference normal tissue samples for cancer research using a deep learning approach. BMC Med. Genomics 12 (Suppl. 1), 21 (2019).

    Google Scholar 

  127. 127.

    Yala, A., Lehman, C., Schuster, T., Portnoi, T. & Barzilay, R. A deep learning mammography-based model for improved breast cancer risk prediction. Radiology 292, 60–66 (2019).

    PubMed  Google Scholar 

  128. 128.

    Topol, E. J. High-performance medicine: the convergence of human and artificial intelligence. Nat. Med. 25, 44–56 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  129. 129.

    Arisdakessian, C., Poirion, O., Yunits, B., Zhu, X. & Garmire, L. DeepImpute: an accurate, fast and scalable deep neural network method to impute single-cell RNA-seq data. Genome Biol. 20, 211 (2018).

    Google Scholar 

  130. 130.

    Eraslan, G., Simon, L. M., Mircea, M., Mueller, N. S. & Theis, F. J. Single-cell RNA-seq denoising using a deep count autoencoder. Nat. Commun. 10, 390 (2019).

    CAS  PubMed  PubMed Central  Google Scholar 

  131. 131.

    Hoadley, K. A. et al. Cell-of-origin patterns dominate the molecular classification of 10,000 tumors from 33 types of cancer. Cell 173, 291–304.e6 (2018).

    CAS  PubMed  PubMed Central  Google Scholar 

  132. 132.

    Yu, L.-X. & Schwabe, R. F. The gut microbiome and liver cancer: mechanisms and clinical translation. Nat. Rev. Gastroenterol. Hepatol. 14, 527–539 (2017).

    PubMed  PubMed Central  Google Scholar 

  133. 133.

    Gao, J. et al. Integrative analysis of complex cancer genomics and clinical profiles using the cBioPortal. Sci. Signal. 6, pl1 (2013).

    PubMed  PubMed Central  Google Scholar 

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This research was supported by grants K01ES025434 awarded by the National Institute of Environmental Health Sciences through funds provided by the trans-NIH Big Data to Knowledge (BD2K) initiative (, P20 COBRE GM103457 awarded by the NIH National Institute of General Medical Sciences, R01 LM012373 awarded by the National Library of Medicine, R01 HD084633 awarded by the National Institute of Child Health and Human Development, and Hawaii Community Foundation Medical Research Grant 14ADVC-64566 to L.X.G; the CJ Huang Foundation, HM Lui Foundation, and TS Kwok Liver Research Foundation to M.S.C; and R21 TR001743 and K01 ES028047 and the MSU Global Impact Initiative to B.C.

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All authors contributed equally to the article.

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Correspondence to Bin Chen.

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Competing interests

R.K.K. declares the following competing interests: research funding and/or supply of study drug to institution for conduct of clinical trials from Adaptimmune, Agios, AstraZeneca, Bayer, Bristol–Myers Squibb, Eli Lilly and Co, EMD Serono, Exelixis, Merck, Novartis, Partner Therapeutics, QED, Taiho; funding (to individual) for Independent Data Monitoring Committee membership by Genentech/Roche; Steering Committee/Advisory Board memberships (funding to institution) by Agios, AstraZeneca, Bristol–Myers Squibb; Steering Committee (without compensation): Exelixis. The other authors declare no competing interests.

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Nature Reviews Gastrenterology & Hepatology thanks J. Lee, D. Sia and C.-M. Wong for their contribution to the peer review of this work.

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Chen, B., Garmire, L., Calvisi, D.F. et al. Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma. Nat Rev Gastroenterol Hepatol 17, 238–251 (2020).

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