- Use of gene expression biomarkers to determine and predict biological activity of of chemicals that can be linked to potential adverse outcomes in several cellular and tissue contexts
- Examples of predictive gene sets that function as MOA biomarker
- Demonstrate data mining of high-throughput test data using MOA biomarkers to identify chemicals with potential adverse outcomes
Dr. Chris Corton:
Chris Corton is a Molecular Toxicologist in the Center for Computational Toxicology and Exposure at the U.S Environmental Protection Agency. He has been studying mechanisms of chemical carcinogenesis for over 30 years focusing on nongenotoxic carcinogenesis. His group is working in a number of areas to 1) reduce the need for animal testing including development of new approach methodologies to allow the prediction of cancer from analysis of gene expression profiles after short-term exposures in animals, 2) build and test tools that allow the interpretation of complex gene expression patterns in chemically-exposed cells to predict potential adverse effects, and 3) apply these tools to understand the potential toxicity of individual chemicals and complex mixtures released from oil and gas operations. He received his Ph.D. degree from the University of Kansas Medical Center followed by a post-doctoral research fellowship at Duke University. From 1989 to 2002, he was a staff scientist at CIIT in Research Triangle Park, NC. In 1994-1995 he was a visiting scientist in the “Orphan Nuclear Receptor” group at Karolinska University, Huddinge, Sweden. He is on the editorial boards of several journals including Associate Editor for Toxicological Sciences. Dr. Corton has served as the Chair of the Society of Toxicology (SOT) Continuing Education and Current Concepts in Toxicology Committees. He is past President of the Molecular and Systems Biology Specialty Section and the outgoing President of the Carcinogenesis Specialty Section. He was the recipient of the SOT 2010 AstraZeneca Traveling Lectureship award and the Toxicology Forum George H. Scott Memorial Award. He has over 200 publications, many related to understanding chemical toxicity using animal-free methods.
Gene expression profiling in the context of toxicity testing in animals has been used for years to assess mode of action, derive points of departure using dose–response modeling, and determine human relevance. High-throughput transcriptomics technologies are increasingly being used to screen environmental chemicals in vitro to identify molecular targets and provide mechanistic context for regulatory testing. This seminar will discuss the use of gene expression biomarkers to make predictions of activity of molecular targets of chemicals that can be linked to adverse outcomes in several cellular and tissue contexts. Gene expression biomarkers are built using global gene expression comparisons from cells or tissues exposed to chemicals with known effects on the factor of interest. Incorporating profiles in which the expression of the factor is altered (e.g., in gene-nullizygous mice) facilitates the identification of predictive genes. Examples of their use will be described. In the first example, biomarkers that predict molecular initiating events (MIEs) and key events (KEs) in liver cancer adverse outcome pathways have been shown to accurately identify chemical–dose combinations in short-term studies that lead to liver cancer in 2-year bioassays. In the second example, batteries of biomarkers are being built for prediction of effects in vitro using profiles from human wildtype and factor-nullizygous cells. The biomarkers are then used to interpret HTTr data streams to populate AOP MIEs and KEs with predictions as part of Tier 1 screening of environmentally relevant chemicals. (This abstract does not represent EPA policy.)
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