Methods and resources to access mutation-dependent effects on cancer drug treatment

Hongcheng Yao, Qian Liang, Xinyi Qian, Junwen Wang, Pak Chung Sham, Mulin Jun Li

Research output: Contribution to journalArticlepeer-review

Abstract

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.

Original languageEnglish (US)
Pages (from-to)1886-1903
Number of pages18
JournalBriefings in bioinformatics
Volume21
Issue number6
DOIs
StatePublished - Nov 1 2020

Keywords

  • actionable mutation
  • bioinformatics tool
  • drug response prediction
  • precision medicine
  • targeted cancer therapy

ASJC Scopus subject areas

  • Information Systems
  • Molecular Biology

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