Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Review Article
  • Published:

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

Nature Reviews Gastroenterology & Hepatology volume 17, pages 238–251 (2020)Cite this article

Subjects

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

This article has been updated

Abstract

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.

This is a preview of subscription content, access via your institution

Access options

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

Similar content being viewed by others

Change history

References

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  4. American Cancer Society. Key statistics about liver cancer. American Cancer Society https://www.cancer.org/cancer/liver-cancer/about/what-is-key-statistics.html (2019).

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  Google Scholar 

  18. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03434379 (2019).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  25. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02508467 (2019).

  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).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. National Cancer Institute. Targeted Cancer Therapies Fact Sheet. NCI https://www.cancer.gov/about-cancer/treatment/types/targeted-therapies/targeted-therapies-fact-sheet (2019).

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  75. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02989857 (2019).

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  80. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT02528643 (2019).

  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).

    Article  CAS  PubMed  Google Scholar 

  82. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT01752933 (2019).

  83. US National Library of Medicine. ClinicalTrials.gov https://clinicaltrials.gov/ct2/show/NCT03257761 (2019).

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

  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).

    Article  PubMed  PubMed Central  CAS  Google Scholar 

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

    Article  PubMed  CAS  Google Scholar 

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

    Article  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  Google Scholar 

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

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  Google Scholar 

  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).

    Article  PubMed  Google Scholar 

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

    Article  CAS  PubMed  Google Scholar 

  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).

    Article  CAS  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  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).

    Article  PubMed  PubMed Central  Google Scholar 

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

    Article  PubMed  PubMed Central  CAS  Google Scholar 

Download references

Acknowledgements

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 (www.bd2k.nih.gov), 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.

Author information

Authors and Affiliations

  1. Department of Pediatrics and Human Development, Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI, USA

    Bin Chen

  2. Department of Computational Medicine and Bioinformatics, University of Michigan, Ann Arbor, MI, USA

    Lana Garmire

  3. Department of Clinical and Experimental Medicine, University of Sassari, Sassari, Italy

    Diego F. Calvisi

  4. Institute of Pathology, University of Regensburg, Regensburg, Germany

    Diego F. Calvisi

  5. Department of Surgery, Asian Liver Center, School of Medicine, Stanford University, Stanford, CA, USA

    Mei-Sze Chua

  6. Department of Medicine, University of California, San Francisco, CA, USA

    Robin K. Kelley

  7. Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, CA, USA

    Xin Chen

Authors
  1. Bin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Lana Garmire
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Diego F. Calvisi
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Mei-Sze Chua
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Robin K. Kelley
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Xin Chen
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

All authors contributed equally to the article.

Corresponding author

Correspondence to Bin Chen.

Ethics declarations

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.

Additional information

Peer review information

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

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Related links

Broad GDAC: https://gdac.broadinstitute.org/

Cancer Cell Line Encyclopedia: https://portals.broadinstitute.org/ccle

Cancer Therapeutics Response Portal: http://portals.broadinstitute.org/ctrp.v2.1/

cBioPortal: http://www.cbioportal.org/

CHNPP Data Portal LIVER: http://liver.cnhpp.ncpsb.org/auth/login

dbGaP: https://www.ncbi.nlm.nih.gov/gap/

EGA portal: http://www.ebi.ac.uk/ega/

Gene Expression Omnibus: https://www.ncbi.nlm.nih.gov/geo/

Genomics of Drug Sensitivity in Cancer Project: https://www.cancerrxgene.org/

GTEx Portal: https://www.gtexportal.org/

Human Proteome Map: http://humanproteomemap.org/

Library of Integrated Network-based Cellular Signatures: http://www.lincscloud.org/

Liver Cancer Model Repository: http://www.picb.ac.cn/limore/

NCI Genomic Data Commons portal: https://portal.gdc.cancer.gov/

PDXLiver: http://www.picb.ac.cn/PDXliver/

Project Achilles: https://depmap.org/portal/achilles/

The Human Protein Atlas: http://www.proteinatlas.org/

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

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). https://doi.org/10.1038/s41575-019-0240-9

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/s41575-019-0240-9

This article is cited by

Search

Advanced search

Quick links

Nature Briefing: Cancer

Sign up for the Nature Briefing: Cancer newsletter — what matters in cancer research, free to your inbox weekly.

Get what matters in cancer research, free to your inbox weekly. Sign up for Nature Briefing: Cancer