In vitro perturbations of targets in cancer hallmark processes predict rodent chemical carcinogenesis

Nicole C. Kleinstreuer, David J. Dix, Keith A. Houck, Robert J. Kavlock, Thomas B. Knudsen, Matthew T. Martin, Katie B. Paul, David M. Reif, Kevin M. Crofton, Kerry Hamilton, Ronald Hunter, Imran Shah, Richard S. Judson

Research output: Contribution to journalArticlepeer-review

70 Scopus citations


Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted testing and functional validation of cancer pathways.

Original languageEnglish (US)
Pages (from-to)40-55
Number of pages16
JournalToxicological Sciences
Issue number1
StatePublished - Jan 2013
Externally publishedYes


  • Bioinformatics.
  • Cancer hallmarks
  • Carcinogenesis
  • In vitro and alternatives
  • Predictive toxicology

ASJC Scopus subject areas

  • Toxicology


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