Using expression and genotype to predict drug response in yeast.

PLoS One
Authors
Keywords
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.

Year of Publication
2009
Journal
PLoS One
Volume
4
Issue
9
Pages
e6907
Date Published
2009 Sep 04
ISSN
1932-6203
DOI
10.1371/journal.pone.0006907
PubMed ID
19730698
PubMed Central ID
PMC2731853
Links
Grant list
P50 GM071508 / GM / NIGMS NIH HHS / United States
50GM071508 / GM / NIGMS NIH HHS / United States
Howard Hughes Medical Institute / United States