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Integrating pathway-based transcriptomic data into quantitative chemical risk assessment: a five chemical case study.

Russell S Thomas¹, Harvey J Clewell, Bruce C Allen

¹The Hamner Institutes for Health Sciences, Research Triangle Park, NC 27709, USA. rthomas@thehamner.org

Mutation Research

|February 7, 2012

Summary

Transcriptomic analysis offers an efficient method for chemical risk assessment, especially for data-poor chemicals. This study correlates gene expression pathways with traditional toxicity endpoints, improving quantitative risk evaluations.

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Area of Science:

Toxicology
Genomics
Environmental Health

Background:

Traditional chemical risk assessments are resource-intensive, limiting data availability.
Many contaminated sites lack reference values for quantitative hazard assessment.
Transcriptomic technology offers a potential solution for evaluating risks of data-poor chemicals.

Purpose of the Study:

To integrate transcriptomic data into chemical risk assessment.
To evaluate dose-response gene expression using a benchmark dose (BMD) approach.
To compare transcriptomic BMDs with traditional toxicity endpoints.

Main Methods:

Mice were exposed to five tumor-forming chemicals for 13 weeks.
Target tissues were analyzed for histological, organ weight, and gene expression changes.
Dose-response gene expression data were analyzed using a BMD approach and pathway analysis.

Main Results:

Transcriptomic BMD values showed a high correlation with traditional non-cancer and cancer endpoints for specific pathways.
Correlated pathways are implicated in non-cancer and cancer disease pathogenesis.
Identified potential toxicity pathways in chemically induced lung and liver responses.

Conclusions:

Transcriptomic pathway analysis can estimate non-cancer and cancer points-of-departure for risk assessment.
This approach enhances quantitative risk assessment for data-poor chemicals.
Identified key toxicity pathways in mouse lung and liver chemical exposures.