Predicting in vitro drug sensitivity using Random Forests.
BACKGROUND
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.


METHODS
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.
Neuro-Oncology Branch, National Cancer Institute, National Institutes of Health, Bethesda, MD, USA. riddickgp@mail.nih.gov
Gregory
G
Riddick
Hua
H
Song
Susie
S
Ahn
Jennifer
J
Walling
Diego
D
Borges-Rivera
Wei
W
Zhang
Howard A
HA
Fine
D000465:Algorithms; D000970:Antineoplastic Agents / Q000494:pharmacology*; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000235:genetics / Q000378:metabolism; D045744:Cell Line, Tumor; D004354:Drug Screening Assays, Antitumor / Q000379:methods*; D005260:Female; D020869:Gene Expression Profiling; D005910:Glioma / Q000235:genetics / Q000378:metabolism; D006801:Humans; D015233:Models, Statistical; D012984:Software
D000970:Antineoplastic Agents