Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines

We demonstrate a method for the prediction of chemotherapeutic response in patients using only before-treatment baseline tumor gene expression data. First, we fitted models for whole-genome gene expression against drug sensitivity in a large panel of cell lines, using a method that allows every gene to influence the prediction. Following data homogenization and filtering, these models were applied to baseline expression levels from primary tumor biopsies, yielding an in vivo drug sensitivity prediction. We validated this approach in three independent clinical trial datasets, and obtained predictions equally good, or better than, gene signatures derived directly from clinical data.

• CC
  Conquer Cancer Foundation
• AS
  American Society of Clinical Oncology
• WT
  Wellcome Trust
• NI
  National Institutes of Health

  Awards: UL1RR024999, CA14599, P30 CA14599
• NC
  National Cancer Institute

  Awards: CA139278, CA14599, P30 CA14599, CA125183
• NC
  National Center for Advancing Translational Sciences

  Award: UL1RR024999