title: Background gene expression networks significantly enhance drug response prediction by transcriptional profiling.
abstract: A central goal of gene expression studies coupled with drug response screens is to identify predictive profiles that can be exploited to stratify patients. Numerous methods have been proposed towards this end, most of them focusing on novel statistical methods and model selection techniques that attempt to uncover groups of genes, whose expression profiles are directly and robustly correlated with drug response. However, biological systems process information through the crosstalk of multiple signaling networks, whose ultimate phenotypic consequences may only be determined by the combined input of relevant interacting systems. By restricting predictive signatures to direct gene-drug correlations, biologically meaningful interactions that may serve as superior predictors are ignored. Here we demonstrate that predictive signatures, which incorporate the interaction between background gene expression patterns and individual predictive probes, can provide superior models than those that directly relate gene expression levels to pharmacological response, and thus should be more widely utilized in pharmacogenetic studies.
authors.affiliation: Department of Molecular and Experimental Medicine, The Scripps Translational Science Institute, The Scripps Research Institute, La Jolla, CA 92037, USA. atorkama@scripps.edu
authors.forename: A
authors.initials: A
authors.lastname: Torkamani
authors.forename: N J
authors.initials: NJ
authors.lastname: Schork
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology; D054316:Biomarkers, Pharmacological; D045744:Cell Line, Tumor / Q000187:drug effects; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects*; D006801:Humans; D009369:Neoplasms / Q000188:drug therapy; D020411:Oligonucleotide Array Sequence Analysis; D015398:Signal Transduction / Q000187:drug effects; D014158:Transcription, Genetic / Q000187:drug effects
chemical_list: D000970:Antineoplastic Agents; D054316:Biomarkers, Pharmacological