Large-scale pharmacogenomic studies and drug response prediction for personalized cancer medicine

作     者：Fangyoumin Feng Bihan Shen Xiaoqin Mou Yixue Lia Hong Li Fangyoumin Feng;Bihan Shen;Xiaoqin Mou;Yixue Li;Hong Li

作者机构：CAS Key Laboratory of Computational BiologyShanghai Institute of Nutrition and HealthUniversity of Chinese Academy of SciencesChinese Academy of SciencesShanghai 200031China Hangzhou Institute for Advanced StudyUniversity of Chinese Academy of SciencesHangzhou 330106China 

出 版 物：《Journal of Genetics and Genomics》 (遗传学报（英文版）)

年 卷 期：2021年第48卷第7期

页      面：540-551页

核心收录：

学科分类：1002[医学-临床医学] 100214[医学-肿瘤学] 10[医学] 

基　　金：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) 

主　　题：Pharmacogenomics Personalized medicine Drug response Biomarkers Deep learning 

摘      要：The response rate of most anti-cancer drugs is limited because of the high heterogeneity of cancer and the complex mechanism of drug *** treatment that stratifies patients into subgroups using molecular biomarkers is promising to improve clinical *** 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 *** 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 ***,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 *** the end,we discuss challenges and propose possible solutions for further improvement.