title: Using expression and genotype to predict drug response in yeast.
abstract: Personalized, or genomic, medicine entails tailoring pharmacological therapies according to individual genetic variation at genomic loci encoding proteins in drug-response pathways. It has been previously shown that steady-state mRNA expression can be used to predict the drug response (i.e., sensitivity or resistance) of non-genotyped mammalian cancer cell lines to chemotherapeutic agents. In a real-world setting, clinicians would have access to both steady-state expression levels of patient tissue(s) and a patient's genotypic profile, and yet the predictive power of transcripts versus markers is not well understood. We have previously shown that a collection of genotyped and expression-profiled yeast strains can provide a model for personalized medicine. Here we compare the predictive power of 6,229 steady-state mRNA transcript levels and 2,894 genotyped markers using a pattern recognition algorithm. We were able to predict with over 70% accuracy the drug sensitivity of 104 individual genotyped yeast strains derived from a cross between a laboratory strain and a wild isolate. We observe that, independently of drug mechanism of action, both transcripts and markers can accurately predict drug response. Marker-based prediction is usually more accurate than transcript-based prediction, likely reflecting the genetic determination of gene expression in this cross.
authors.affiliation: Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts, United States of America.
authors.forename: Douglas M
authors.initials: DM
authors.lastname: Ruderfer
authors.forename: David C
authors.initials: DC
authors.lastname: Roberts
authors.forename: Stuart L
authors.initials: SL
authors.lastname: Schreiber
authors.forename: Ethan O
authors.initials: EO
authors.lastname: Perlstein
authors.forename: Leonid
authors.initials: L
authors.lastname: Kruglyak
mesh_terms: D000465:Algorithms; D002626:Chemistry, Pharmaceutical / Q000379:methods; D019295:Computational Biology / Q000379:methods; D004353:Drug Evaluation, Preclinical / Q000379:methods; D020869:Gene Expression Profiling; D015966:Gene Expression Regulation, Fungal; D008040:Genetic Linkage; D005819:Genetic Markers; D023281:Genomics; D005838:Genotype; D012333:RNA, Messenger / Q000378:metabolism; D015203:Reproducibility of Results
chemical_list: D005819:Genetic Markers; D012333:RNA, Messenger