Identification of toxicologically predictive gene sets using cDNA microarrays.
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.
Thomas
R S
RS
McArdle Laboratory for Cancer Research, University of Wisconsin Medical School, Madison, Wisconsin 53706-1599, USA.
Rank
D R
DR
Penn
S G
SG
Zastrow
G M
GM
Hayes
K R
KR
Pande
K
K
Glover
E
E
Silander
T
T
Craven
M W
MW
Reddy
J K
JK
Jovanovich
S B
SB
Bradfield
C A
CA
D000818:Animals; D064420:Drug-Related Side Effects and Adverse Reactions / Q000235:genetics*; D015870:Gene Expression / Q000187:drug effects; D020869:Gene Expression Profiling; D008297:Male; D051379:Mice; D008810:Mice, Inbred C57BL; D023421:Models, Animal; D020411:Oligonucleotide Array Sequence Analysis / Q000379:methods; D004364:Pharmaceutical Preparations / Q000145:classification; D011237:Predictive Value of Tests; D012313:RNA / Q000096:biosynthesis / Q000187:drug effects; D015398:Signal Transduction
D004364:Pharmaceutical Preparations; D012313:RNA