Harnessing big ‘omics’ data and AI for drug discovery in hepatocellular carcinoma
Open accessAbstract
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.
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Heterogeneity, or diverse cell populations within diseased tissue, is the leading cause of treatment failure for many complex diseases and a major obstacle to precision medicine.
“Heterogeneity, or more specifically, the diverse cell populations within the diseased tissue, is the leading cause of treatment failure for many complex diseases, such as cancers1, Alzheimer’s disease2, stroke3, and coronavirus disease 2019 (COVID-19)4, etc., as well as a major obstacle to successful precision medicine5–7.”
From ASGARD is a single-cell guided pipeline to aid repurposing of drugs (2023) · cites this paper as [7]
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ASGARD is a single-cell guided pipeline to aid repurposing of drugs
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Self-citation
- Authors
- Chen, B., Garmire, L., Calvisi, D. F., Chua, M.-S., Kelley, R. K., & Chen, X.
- Affiliations
- Michigan State University (US)
- University of Michigan (US)
- University of Sassari (IT)
- Stanford Medicine (US)
- University of California, San Francisco (US)
- Corresponding author
- Chen, Bin
- Venue
- Nature Reviews Gastroenterology & Hepatology
- Published
- 2020-01-03
- Volume
- 17
- Issue
- 4
- Pages
- 238-251
- ISSN
- 1759-5045 (ISSN-L)
- 1759-5053
- PMC
- PMC7401304
- OpenAlex
- W2997782431
- Cited by
- 154
- References
- 133
- Citations by year
- 2026: 7 · 2025: 25 · 2024: 20 · 2023: 40 · 2022: 19 · 2021: 26 · 2020: 17
- FWCI
- 3.53
- Type
- journal article
- Language
- en
- Update notices
- correction (2020-03-11)
- Open access
- green
- www.ncbi.nlm.nih.gov
- Locations
- Nature Reviews Gastroenterology & Hepatology (publishedVersion)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- University of Regensburg Publication Server (University of Regensburg) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion)
- UnissResearch (Università degli Studi di Sassari) (submittedVersion)
- Funders
- National Institutes of Health (R21TR001743)
- National Institute of General Medical Sciences (P20GM103457)
- National Institute of Environmental Health Sciences (K01ES025434, K01ES028047)
- U.S. National Library of Medicine (R01LM012907, R01LM012373)
- National Center for Advancing Translational Sciences
- Eunice Kennedy Shriver National Institute of Child Health and Human Development (R01HD084633)
- Topics
- Health Sciences › Medicine › Hepatology › Hepatocellular Carcinoma Treatment and Prognosis
- Physical Sciences › Computer Science › Computational Theory and Mathematics › Computational Drug Discovery Methods
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Cancer Research › Cancer Genomics and Diagnostics
- Keywords
- hepatocellular carcinoma, drug discovery, artificial intelligence, public datasets, drug target identification, treatment response biomarkers, single-cell profiling, multi-omics integration, precision oncology
- MeSH terms
- Machine Learning, Animals, Antineoplastic Agents, Artificial Intelligence, Disease Models, Animal, Carcinoma, Hepatocellular, Humans, Liver Neoplasms, Biomarkers, Genomics, Cell Line, Tumor, Drug Discovery, Translational Research, Biomedical, Precision Medicine, Molecular Targeted Therapy
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