MOTIVATION: 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). RESULTS: 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. IMPLEMENTATION: 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.
MOTIVATION: 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). RESULTS: 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. IMPLEMENTATION: 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.
Authors: J E Staunton; D K Slonim; H A Coller; P Tamayo; M J Angelo; J Park; U Scherf; J K Lee; W O Reinhold; J N Weinstein; J P Mesirov; E S Lander; T R Golub Journal: Proc Natl Acad Sci U S A Date: 2001-09-11 Impact factor: 11.205
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Authors: K D Paull; R H Shoemaker; L Hodes; A Monks; D A Scudiero; L Rubinstein; J Plowman; M R Boyd Journal: J Natl Cancer Inst Date: 1989-07-19 Impact factor: 13.506
Authors: Jae K Lee; Dmytro M Havaleshko; Hyungjun Cho; John N Weinstein; Eric P Kaldjian; John Karpovich; Andrew Grimshaw; Dan Theodorescu Journal: Proc Natl Acad Sci U S A Date: 2007-07-31 Impact factor: 11.205
Authors: Aiguo Li; Jennifer Walling; Yuri Kotliarov; Angela Center; Mary Ellen Steed; Susie J Ahn; Mark Rosenblum; Tom Mikkelsen; Jean Claude Zenklusen; Howard A Fine Journal: Mol Cancer Res Date: 2008-01-09 Impact factor: 5.852
Authors: Zachary A Gurard-Levin; Laurence O W Wilson; Vera Pancaldi; Sophie Postel-Vinay; Fabricio G Sousa; Cecile Reyes; Elisabetta Marangoni; David Gentien; Alfonso Valencia; Yves Pommier; Paul Cottu; Geneviève Almouzni Journal: Mol Cancer Ther Date: 2016-05-16 Impact factor: 6.261