Prediction of Compound Signature Using High Density Gene Expression Profiling

Abstract

DNA microarrays, used to measure the gene expression of thousands of genes simultaneously, hold promise for future application in efficient screening of therapeutic drugs. This will be aided by the development and population of a database with gene expression profiles corresponding to biological responses to exposures to known compounds whose toxicological and pathological endpoints are well characterized. Such databases could then be interrogated, using profiles corresponding to biological responses to drugs after developmental or environmental exposures. A positive correlation with an archived profile could lead to some knowledge regarding the potential effects of the tested compound or exposure. We have previously shown that cDNA microarrays can be used to generate chemical-specific gene expression profiles that can be distinguished across and within compound classes, using clustering, simple correlation, or principal component analyses. In this report, we test the hypothesis that knowledge can be gained regarding the nature of blinded samples, using an initial training set comprised of gene expression profiles derived from rat liver exposed to clofibrate, Wyeth 14,643, gemfibrozil, or phenobarbital for 24 h or 2 weeks of exposure. Highly discriminant genes were derived from our database training set using approaches including linear discriminant analysis (LDA) and genetic algorithm/K-nearest neighbors (GA/KNN). Using these genes in the analysis of coded liver RNA samples derived from 24-h, 3-day, or 2-week exposures to phenytoin, diethylhexylpthalate, or hexobarbital led to successful prediction of whether these samples were derived from livers of rats exposed to enzyme inducers or to peroxisome proliferators. This validates our initial hypothesis and lends credibility to the concept that the further development of a gene expression database for chemical effects will greatly enhance the hazard identification processes.

Comparison of the Predictive Accuracy of DNA Array-Based Multigene Classifiers across cDNA Arrays and Affymetrix GeneChips

“…11 This method has previously been applied successfully to high-dimensional gene expression and proteomic profile data. [13][14][15][16] A potential advantage of this approach is that the GA can identify combination of genes that are predictive together even if individual genes that contribute to the classifier have limited predictive value and would be missed by two-sample t statistics. However, to reduce computation time, we performed GA/LDA on a subset of the data that was enriched in informative genes.…”

Identification of Putative Gene Based Markers of Renal Toxicity

“…The cDNA chips were scanned with an Axon Scanner (Axon Instruments, Foster City, CA) using independent laser excitation of the two fluors at 532 and 635 nm wavelengths for the Cy3 and Cy5 labels, respectively. Image analysis, background subtraction, normalization, and determination of significant gene changes were conducted using the ArraySuite, version 2.0 extensions of the IPLab image processing software package (Scanalytics, Fairfax, VA) (Chen et al 1997) as previously described (Bushel et al 2001Hamadeh et al 2002aHamadeh et al , 2002bHamadeh et al , 2002c.…”

“…Recent application of microarray-based gene profiling has provided some unique insights into compound-specific and toxicity-related gene expression changes in the liver (Hamadeh et al 2002a(Hamadeh et al , 2002bWaring et al 2001aWaring et al , 2001b, and several laboratories have applied similar techniques to identify gene expression changes related to nephrotoxicity (Huang et al 2001;Nagasawa et al 2001;Yoshida et al 2002).…”

Value of new biomarkers for safety testing in drug development

“…It is based on the measurement of thousands of genes simultaneously and has shown potential to revolutionize toxicity testing. It has been successfully used as a tool to elucidate mechanisms of toxicity, as well as having the potential to predict toxicities much earlier during drug development [2][3][4]. The advanced knowledge of gene-and protein-expression patterns, together with modern classification algorithms, has also demonstrated practical benefits for predicting pathological events and toxic end points [5][6].…”