Predicting in vitro drug sensitivity using Random Forests

Bioinformatics. 2011 Jan 15;27(2):220-4. doi: 10.1093/bioinformatics/btq628. Epub 2010 Dec 5.


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

Publication types

  • Evaluation Study
  • Research Support, N.I.H., Intramural

MeSH terms

  • Algorithms
  • Antineoplastic Agents / pharmacology*
  • Artificial Intelligence*
  • Breast Neoplasms / genetics
  • Breast Neoplasms / metabolism
  • Cell Line, Tumor
  • Drug Screening Assays, Antitumor / methods*
  • Female
  • Gene Expression Profiling*
  • Glioma / genetics
  • Glioma / metabolism
  • Humans
  • Models, Statistical
  • Software


  • Antineoplastic Agents