Drug response prediction by ensemble learning and drug-induced gene expression signatures

Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in addition to cytotoxicity databases. These large sets...

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Bibliographic Details
Published in:Genomics (San Diego, Calif.) Calif.), 2019-09, Vol.111 (5), p.1078-1088
Main Authors: Tan, Mehmet, Özgül, Ozan Fırat, Bardak, Batuhan, Ekşioğlu, Işıksu, Sabuncuoğlu, Suna
Format: Article
Language:English
Subjects:
Animals
Antineoplastic Agents - pharmacology
Antineoplastic Agents - toxicity
Cell line signatures
Cell Line, Tumor
Cell Survival - drug effects
Drug Resistance, Neoplasm
Drug response prediction
Drug signatures
Ensemble learning
Gene Expression Profiling - methods
Humans
Inhibitory Concentration 50
Machine Learning
Software
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Summary:Chemotherapeutic response of cancer cells to a given compound is one of the most fundamental information one requires to design anti-cancer drugs. Recently, considerable amount of drug-induced gene expression data has become publicly available, in addition to cytotoxicity databases. These large sets of data provided an opportunity to apply machine learning methods to predict drug activity. However, due to the complexity of cancer drug mechanisms, none of the existing methods is perfect. In this paper, we propose a novel ensemble learning method to predict drug response. In addition, we attempt to use the drug screen data together with two novel signatures produced from the drug-induced gene expression profiles of cancer cell lines. Finally, we evaluate predictions by in vitro experiments in addition to the tests on data sets. The predictions of the methods, the signatures and the software are available from http://mtan.etu.edu.tr/drug-response-prediction/. •We proposed novel signatures to represent cell line sensitivity and drug activity.•We designed an ensemble learning method to predict anti cancer drug response based on stacked generalization.•The performance was evaluated using both in silico and in vitro methods.•We integrated drug induced gene expression data with large scale cell viability screens.
ISSN:0888-7543
1089-8646
DOI:10.1016/j.ygeno.2018.07.002