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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

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.

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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

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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.

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Correspondence to Bin Chen.

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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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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/

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

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