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A Three-Dimensional Spheroid Model to Investigate the Tumor-Stromal Interaction in Hepatocellular Carcinoma
Published on: September 30, 2021
Harnessing big 'omics' data and AI for drug discovery in hepatocellular carcinoma
Bin Chen1, Lana Garmire2, Diego F Calvisi3,4
1Department of Pediatrics and Human Development, Department of Pharmacology and Toxicology, Michigan State University, Grand Rapids, MI, USA. bin.chen@hc.msu.edu.
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.
Insights
New targeted therapies and immunotherapies offer hope for hepatocellular carcinoma (HCC) patients. Artificial intelligence applied to public data may reveal optimal treatment strategies and biomarkers for liver cancer.
Area of Science:
- Oncology
- Genomics
- Bioinformatics
Background:
- Hepatocellular carcinoma (HCC) is a primary liver cancer with limited treatment options historically.
- Sorafenib was the sole approved drug for HCC for nearly a decade.
- Recent clinical trials show efficacy for targeted therapies, anti-angiogenic agents, and immune checkpoint inhibitors.
Purpose of the Study:
- To review public datasets for hepatocellular carcinoma (HCC).
- To explore the application of artificial intelligence (AI) in analyzing HCC data.
- To identify new therapeutic targets, drugs, and biomarkers for HCC treatment response.
Main Methods:
- Comprehensive review of publicly available HCC datasets.
- Discussion of emerging artificial intelligence (AI) methodologies.
- Analysis of molecular profiling data (genomics, transcriptomics, proteomics, metabolomics).
Main Results:
- Multiple new agents demonstrate efficacy in HCC clinical trials.
- Optimal sequencing and choice of HCC therapies remain undetermined.
- Lack of established biomarkers hinders personalized treatment selection.
- Publicly available HCC datasets offer opportunities for novel insights.
Conclusions:
- Advances in molecular measurement technologies facilitate comprehensive HCC profiling.
- AI can leverage public HCC data to uncover new therapeutic targets and drugs.
- AI-driven analysis of HCC data can guide treatment choices and identify biomarkers.
- Integrating AI with multi-omics data holds promise for improving HCC patient outcomes.
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