生物信息学
抗癌药物
计算生物学
癌细胞系
药品
药物反应
药物发现
计算机科学
癌症
预测建模
机器学习
生物信息学
癌细胞
生物
药理学
遗传学
基因
作者
Michael P. Menden,Francesco Iorio,Mathew J. Garnett,Ultan McDermott,Cyril H. Benes,Pedro J. Ballester,Julio Sáez-Rodríguez
出处
期刊:PLOS ONE
[Public Library of Science]
日期:2013-04-30
卷期号:8 (4): e61318-e61318
被引量:416
标识
DOI:10.1371/journal.pone.0061318
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 R² of 0.72 and 0.64 respectively. Furthermore, models were able to predict with comparable accuracy (R² 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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