ToxSci Advance Access originally published online on November 17, 2006
Toxicological Sciences 2007 96(1):40-46; doi:10.1093/toxsci/kfl171
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A Comparison of Transcriptomic and Metabonomic Technologies for Identifying Biomarkers Predictive of Two-Year Rodent Cancer Bioassays
* CIIT Centers for Health Research, Research Triangle Park, North Carolina 27709-2137
Division of Molecular Pharmaceutics, School of Pharmacy, University of North Carolina, Chapel Hill, North Carolina 27599
SAS Institute Inc., Cary, North Carolina 27513-2414
1 To whom correspondence should be addressed at CIIT Centers for Health Research, 6 Davis Drive, Research Triangle Park, NC 27709-2137. Fax: (919) 558-1300. E-mail: rthomas{at}ciit.org.
Received September 25, 2006; accepted November 14, 2006
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Abstract |
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Two-year rodent bioassays play a central role in evaluating the carcinogenic potential of both commercial products and environmental contaminants. The bioassays are expensive and time consuming, requiring years to complete and costing $2–4 million. In this study, we compare transcriptomic and metabonomic technologies for discovering biomarkers that can efficiently and economically identify chemical carcinogens without performing a standard two-year rodent bioassay. Animals were exposed subchronically to two chemicals (one genotoxic and one nongenotoxic) that were positive for lung and liver tumors in a standard two-year bioassay, two chemicals that were negative, and two control groups. Microarray analysis performed on liver and lung tissues identified multiple biomarkers in each tissue that could discriminate between carcinogenic and noncarcinogenic treatments. The discriminating biomarkers shared a common expression profile among carcinogenic treatments despite different genotoxicity categories and potential modes of action, suggesting that they reflect underlying cellular changes in the transition toward neoplasia. Statistical classification analysis exhibited 100% accuracy in both tissues when the number of genes was less than 5000. Additional genes reduced the predictive accuracy of the model. Serum samples were analyzed by 1H nuclear magnetic resonance (NMR) spectroscopy, and chemical-specific metabolites were removed from the spectra. The statistical classification analysis of the endogenous serum metabolites showed relatively low predictive accuracy with few metabolites in the model, but the accuracy increased to a maximum of 94% when all metabolites were added. These results suggest that individual endogenous metabolites are relatively poor biomarkers, but the metabolite profile as a whole is altered following carcinogen treatment.
Key Words: genomics; metabonomics; biomarkers; rodent cancer bioassays.