Bihan Shen
Xiaoqin Mou
Yixue Li
Hong Li
a CAS Key Laboratory of Computational Biology, Shanghai Institute of Nutrition and Health, University of Chinese Academy of Sciences, Chinese Academy of Sciences, Shanghai 200031, China;
b Hangzhou Institute for Advanced Study, University of Chinese Academy of Sciences, Hangzhou 330106, China
Funds: This work was supported by National Key Research and Development Project (2019YFC1315804), National Natural Science Foundation of China (31771472), Chinese Academy of Sciences (ZDBSSSW-DQC-02), SA-SIBS Scholarship Program, Shanghai Municipal Science and Technology Major Project (No.2018SHZDZX01), CAS Youth Innovation Promotion Association (2018307), Chinese Academy of Sciences (KFJ-STS-QYZD-126).
Received Date: 2021-01-22
Accepted Date:2021-03-28
Rev Recd Date:2021-03-26
Publish Date:2021-07-20
Abstract
Abstract
The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug action. Personalized treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical benefit. With the accumulation of preclinical models and advances in computational approaches of drug response prediction, pharmacogenomics has made great success over the last 20 years and is increasingly used in the clinical practice of personalized cancer medicine. In this article, we first summarize FDA-approved pharmacogenomic biomarkers and large-scale pharmacogenomic studies of preclinical cancer models such as patient-derived cell lines, organoids, and xenografts. Furthermore, we comprehensively review the recent developments of computational methods in drug response prediction, covering network, machine learning, and deep learning technologies and strategies to evaluate immunotherapy response. In the end, we discuss challenges and propose possible solutions for further improvement.Keywords: Pharmacogenomics,
Personalized medicine,
Drug response,
Biomarkers,
Deep learning
PDF全文下载地址:
http://www.jgenetgenomics.org/article/exportPdf?id=c5718b2c-568f-45f9-a5bd-4c56b2429218&language=en
