Journal
BRIEFINGS IN BIOINFORMATICS
Volume 21, Issue 6, Pages 1886-1903Publisher
OXFORD UNIV PRESS
DOI: 10.1093/bib/bbz109
Keywords
precision medicine; actionable mutation; targeted cancer therapy; drug response prediction; bioinformatics tool
Funding
- National Natural Science Foundation of China [31701143, 31871327]
- Natural Science Foundation of Tianjin [18JCZDJC34700]
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In clinical cancer treatment, genomic alterations would often affect the response of patients to anticancer drugs. Studies have shown that molecular features of tumors could be biomarkers predictive of sensitivity or resistance to anticancer agents, but the identification of actionable mutations are often constrained by the incomplete understanding of cancer genomes. Recent progresses of next-generation sequencing technology greatly facilitate the extensive molecular characterization of tumors and promote precision medicine in cancers. More and more clinical studies, cancer cell lines studies, CRISPR screening studies as well as patient-derived model studies were performed to identify potential actionable mutations predictive of drug response, which provide rich resources of molecularly and pharmacologically profiled cancer samples at different levels. Such abundance of data also enables the development of various computational models and algorithms to solve the problem of drug sensitivity prediction, biomarker identification and in silico drug prioritization by the integration of multiomics data. Here, we review the recent development of methods and resources that identifies mutation-dependent effects for cancer treatment in clinical studies, functional genomics studies and computational studies and discuss the remaining gaps and future directions in this area.
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