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Environ Mol Mutagen. 2015 Jul;56(6):505-19. doi: 10.1002/em.21941. Epub 2015 Mar 2.

Development of a toxicogenomics signature for genotoxicity using a dose-optimization and informatics strategy in human cells.

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Department of Biochemistry and Molecular and Cellular Biology, Georgetown University Medical Center, Washington, DC.
Department of Oncology, Georgetown University Medical Center, Washington, DC.
Biological Engineering Department, Utah State University, Logan, Utah.
Pfizer Global Research and Development, Drug Safety Research and Development, Groton, Connecticut, 06340.
Environmental Health Science and Research Bureau, Health Canada, Ottawa, Ontario, K1A 0K9, Canada.
Center of Excellence In Genomic Medicine Research (CEGMR), King Abdulaziz University, Jeddah, Saudi Arabia.


The development of in vitro molecular biomarkers to accurately predict toxicological effects has become a priority to advance testing strategies for human health risk assessment. The application of in vitro transcriptomic biomarkers promises increased throughput as well as a reduction in animal use. However, the existing protocols for predictive transcriptional signatures do not establish appropriate guidelines for dose selection or account for the fact that toxic agents may have pleiotropic effects. Therefore, comparison of transcriptome profiles across agents and studies has been difficult. Here we present a dataset of transcriptional profiles for TK6 cells exposed to a battery of well-characterized genotoxic and nongenotoxic chemicals. The experimental conditions applied a new dose optimization protocol that was based on evaluating expression changes in several well-characterized stress-response genes using quantitative real-time PCR in preliminary dose-finding studies. The subsequent microarray-based transcriptomic analyses at the optimized dose revealed responses to the test chemicals that were typically complex, often exhibiting substantial overlap in the transcriptional responses between a variety of the agents making analysis challenging. Using the nearest shrunken centroids method we identified a panel of 65 genes that could accurately classify toxicants as genotoxic or nongenotoxic. To validate the 65-gene panel as a genomic biomarker of genotoxicity, the gene expression profiles of an additional three well-characterized model agents were analyzed and a case study demonstrating the practical application of this genomic biomarker-based approach in risk assessment was performed to demonstrate its utility in genotoxicity risk assessment.


DNA-damage inducible; bioinformatics; stress genes; super-paramagnetic clustering

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