Predicting in vitro drug sensitivity using Random Forests

• Published in: Bioinformatics Vol. 27; no. 2; pp. 220 - 224
• Main Authors: RIDDICK, Gregory, HUA SONG, AHN, Susie, WALLING, Jennifer, BORGES-RIVERA, Diego, WEI ZHANG, FINE, Howard A
• Format: Journal Article
• Language: English
• Published: Oxford Oxford University Press 15-01-2011
• Subjects: Algorithms; Antineoplastic Agents - pharmacology; Artificial Intelligence; Bioinformatics; Biological and medical sciences; Breast Neoplasms - genetics; Breast Neoplasms - metabolism; Cell Line, Tumor; Construction; Drug Screening Assays, Antitumor - methods; Drugs; Female; Fundamental and applied biological sciences. Psychology; Gene expression; Gene Expression Profiling; General aspects; Glioma - genetics; Glioma - metabolism; Humans; In vitro testing; Mathematical models; Mathematics in biology. Statistical analysis. Models. Metrology. Data processing in biology (general aspects); Models, Statistical; Original Papers; Panels; Regression; Software; Drug; In vitro; Sensitivity
• ISSN: 1367-4803; 1367-4811; 1460-2059
• DOI: 10.1093/bioinformatics/btq628

Panels of cell lines such as the NCI-60 have long been used to test drug candidates for their ability to inhibit proliferation. Predictive models of in vitro drug sensitivity have previously been constructed using gene expression signatures generated from gene expression microarrays. These statistical models allow the prediction of drug response for cell lines not in the original NCI-60. We improve on existing techniques by developing a novel multistep algorithm that builds regression models of drug response using Random Forest, an ensemble approach based on classification and regression trees (CART).
This method proved successful in predicting drug response for both a panel of 19 Breast Cancer and 7 Glioma cell lines, outperformed other methods based on differential gene expression, and has general utility for any application that seeks to relate gene expression data to a continuous output variable.
Software was written in the R language and will be available together with associated gene expression and drug response data as the package ivDrug at http://r-forge.r-project.org.