Anticancer drug sensitivity prediction in cell lines from baseline gene expression through recursive feature selection

BMC Cancer

Research Article

Haiyun Wang¹  Chun Li²  Xiaoqi Zheng³  Jun Wang³  Zuoli Dong³  Yun Fang³  Naiqian Zhang³ 

[1] Department of Bioinformatics, School of Life Science and Technology, Tongji University, Shanghai, China;Department of Mathematics, Bohai University, Jinzhou, China;Department of Mathematics, Shanghai Normal University, Shanghai, China;

关键词: Drug sensitivity prediction;    Feature selection;    Recursive feature elimination;   

DOI  :  10.1186/s12885-015-1492-6

 received in 2014-09-11, accepted in 2015-06-16,  发布年份 2015

来源: Springer

【 摘 要 】

BackgroundAn enduring challenge in personalized medicine is to select right drug for individual patients. Testing drugs on patients in large clinical trials is one way to assess their efficacy and toxicity, but it is impractical to test hundreds of drugs currently under development. Therefore the preclinical prediction model is highly expected as it enables prediction of drug response to hundreds of cell lines in parallel.MethodsRecently, two large-scale pharmacogenomic studies screened multiple anticancer drugs on over 1000 cell lines in an effort to elucidate the response mechanism of anticancer drugs. To this aim, we here used gene expression features and drug sensitivity data in Cancer Cell Line Encyclopedia (CCLE) to build a predictor based on Support Vector Machine (SVM) and a recursive feature selection tool. Robustness of our model was validated by cross-validation and an independent dataset, the Cancer Genome Project (CGP).ResultsOur model achieved good cross validation performance for most drugs in the Cancer Cell Line Encyclopedia (≥80 % accuracy for 10 drugs, ≥ 75 % accuracy for 19 drugs). Independent tests on eleven common drugs between CCLE and CGP achieved satisfactory performance for three of them, i.e., AZD6244, Erlotinib and PD-0325901, using expression levels of only twelve, six and seven genes, respectively.ConclusionsThese results suggest that drug response could be effectively predicted from genomic features. Our model could be applied to predict drug response for some certain drugs and potentially play a complementary role in personalized medicine.

【 授权许可】

Unknown   
© Dong et al. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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