title: Clinical drug response can be predicted using baseline gene expression levels and in vitro drug sensitivity in cell lines.
abstract: 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.
authors.forename: Paul
authors.initials: P
authors.lastname: Geeleher
authors.forename: Nancy J
authors.initials: NJ
authors.lastname: Cox
authors.forename: R Stephanie
authors.initials: RS
authors.lastname: Huang
mesh_terms: D000465:Algorithms; D000970:Antineoplastic Agents / Q000627:therapeutic use; D014408:Biomarkers, Tumor / Q000235:genetics* / Q000378:metabolism; D001943:Breast Neoplasms / Q000188:drug therapy; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy; D045744:Cell Line, Tumor; D019008:Drug Resistance, Neoplasm / Q000235:genetics*; D005260:Female; D015972:Gene Expression Regulation, Neoplastic; D015894:Genome, Human; D006801:Humans; D008957:Models, Genetic; D009101:Multiple Myeloma / Q000188:drug therapy; D011237:Predictive Value of Tests
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor