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Using Expression and Genotype to Predict Drug Response in Yeast

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  • Douglas M Ruderfer
  • David C Roberts
  • Stuart L Schreiber
  • Ethan O Perlstein
  • Leonid Kruglyak

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.

Suggested Citation

  • Douglas M Ruderfer & David C Roberts & Stuart L Schreiber & Ethan O Perlstein & Leonid Kruglyak, 2009. "Using Expression and Genotype to Predict Drug Response in Yeast," PLOS ONE, Public Library of Science, vol. 4(9), pages 1-7, September.
    • Handle: RePEc:plo:pone00:0006907
    DOI: 10.1371/journal.pone.0006907
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    References listed on IDEAS

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    1. Jesse J Swen & Tom W Huizinga & Hans Gelderblom & Elisabeth G E de Vries & Willem J J Assendelft & Julia Kirchheiner & Henk-Jan Guchelaar, 2007. "Translating Pharmacogenomics: Challenges on the Road to the Clinic," PLOS Medicine, Public Library of Science, vol. 4(8), pages 1-8, August.
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    Cited by:

    1. Yu Takagi & Hirokazu Matsuda & Yukio Taniguchi & Hiroaki Iwaisaki, 2014. "Predicting the Phenotypic Values of Physiological Traits Using SNP Genotype and Gene Expression Data in Mice," PLOS ONE, Public Library of Science, vol. 9(12), pages 1-17, December.

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