Machine Learning Prediction of Cancer Cell Sensitivity to Drugs Based on Genomic and Chemical Properties

被引:353
作者
Menden, Michael P. [1 ]
Iorio, Francesco [1 ,2 ]
Garnett, Mathew [2 ]
McDermott, Ultan [2 ]
Benes, Cyril H. [3 ,4 ]
Ballester, Pedro J. [1 ]
Saez-Rodriguez, Julio [1 ]
机构
[1] Wellcome Trust Genome Campus Cambridge, European Bioinformat Inst, Cambridge, England
[2] Wellcome Trust Genome Campus Cambridge, Wellcome Trust Sanger Inst, Canc Genome Project, Cambridge, England
[3] Massachusetts Gen Hosp, Ctr Canc, Ctr Mol Therapeut, Charlestown, MA USA
[4] Harvard Univ, Sch Med, Charlestown, MA USA
基金
英国医学研究理事会; 英国惠康基金;
关键词
GENE-EXPRESSION SIGNATURES; INTEGRATIVE ANALYSIS; SMALL MOLECULES; ANTICANCER; DISCOVERY; PATTERNS; COMPARE; QSAR;
D O I
10.1371/journal.pone.0061318
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Predicting the response of a specific cancer to a therapy is a major goal in modern oncology that should ultimately lead to a personalised treatment. High-throughput screenings of potentially active compounds against a panel of genomically heterogeneous cancer cell lines have unveiled multiple relationships between genomic alterations and drug responses. Various computational approaches have been proposed to predict sensitivity based on genomic features, while others have used the chemical properties of the drugs to ascertain their effect. In an effort to integrate these complementary approaches, we developed machine learning models to predict the response of cancer cell lines to drug treatment, quantified through IC50 values, based on both the genomic features of the cell lines and the chemical properties of the considered drugs. Models predicted IC50 values in a 8-fold cross-validation and an independent blind test with coefficient of determination R-2 of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R-2 of 0.61) IC50s of cell lines from a tissue not used in the training stage. Our in silico models can be used to optimise the experimental design of drug-cell screenings by estimating a large proportion of missing IC50 values rather than experimentally measuring them. The implications of our results go beyond virtual drug screening design: potentially thousands of drugs could be probed in silico to systematically test their potential efficacy as anti-tumour agents based on their structure, thus providing a computational framework to identify new drug repositioning opportunities as well as ultimately be useful for personalized medicine by linking the genomic traits of patients to drug sensitivity.
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