Identification of toxicologically predictive gene sets using cDNA microarrays

R. S. Thomas, D. R. Rank, S. G. Penn, G. M. Zastrow, K. R. Hayes, K. Pande, E. Glover, T. Silander, M. W. Craven, J. K. Reddy, S. B. Jovanovich, C. A. Bradfield*

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

217 Scopus citations

Abstract

We have developed an approach to classify toxicants based upon their influence on profiles of mRNA transcripts. Changes in liver gene expression were examined after exposure of mice to 24 model treatments that fall into five well-studied toxicological categories: peroxisome proliferators, aryl hydrocarbon receptor agonists, noncoplanar polychlorinated biphenyls, inflammatory agents, and hypoxia-inducing agents. Analysis of 1200 transcripts using both a correlation-based approach and a probabilistic approach resulted in a classification accuracy of between 50 and 70%. However, with the use of a forward parameter selection scheme, a diagnostic set of 12 transcripts was identified that provided an estimated 100% predictive accuracy based on leave-one-out cross-validation. Expansion of this approach to additional chemicals of regulatory concern could serve as an important screening step in a new era of toxicological testing.

Original languageEnglish (US)
Pages (from-to)1189-1194
Number of pages6
JournalMolecular pharmacology
Volume60
Issue number6
DOIs
StatePublished - 2001

ASJC Scopus subject areas

  • Molecular Medicine
  • Pharmacology

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