A new approach methodology to identify tumorigenic chemicals using short-term exposures and transcript profiling.

2-year cancer bioassay adverse outcome pathway biomarkers liver cancer new approach methodologies transcript profiling

Journal

Frontiers in toxicology
ISSN: 2673-3080
Titre abrégé: Front Toxicol
Pays: Switzerland
ID NLM: 101777990

Informations de publication

Date de publication:
2024
Historique:
received: 23 04 2024
accepted: 27 09 2024
medline: 1 11 2024
pubmed: 1 11 2024
entrez: 1 11 2024
Statut: epublish

Résumé

Current methods for cancer risk assessment are resource-intensive and not feasible for most of the thousands of untested chemicals. In earlier studies, we developed a new approach methodology (NAM) to identify liver tumorigens using gene expression biomarkers and associated tumorigenic activation levels (TALs) after short-term exposures in rats. The biomarkers are used to predict the six most common rodent liver cancer molecular initiating events. In the present study, we wished to confirm that our approach could be used to identify liver tumorigens at only one time point/dose and if the approach could be applied to (targeted) RNA-Seq analyses. Male rats were exposed for 4 days by daily gavage to 15 chemicals at doses with known chronic outcomes and liver transcript profiles were generated using Affymetrix arrays. Our approach had 75% or 85% predictive accuracy using TALs derived from the TG-GATES or DrugMatrix studies, respectively. In a dataset generated from the livers of male rats exposed to 16 chemicals at up to 10 doses for 5 days, we found that our NAM coupled with targeted RNA-Seq (TempO-Seq) could be used to identify tumorigenic chemicals with predictive accuracies of up to 91%. Overall, these results demonstrate that our NAM can be applied to both microarray and (targeted) RNA-Seq data generated from short-term rat exposures to identify chemicals, their doses, and mode of action that would induce liver tumors, one of the most common endpoints in rodent bioassays.

Identifiants

pubmed: 39483698
doi: 10.3389/ftox.2024.1422325
pii: 1422325
pmc: PMC11526388
doi:

Types de publication

Journal Article

Langues

eng

Pagination

1422325

Informations de copyright

Copyright © 2024 Ledbetter, Auerbach, Everett, Vallanat, Lowit, Akerman, Gwinn, Wehmas, Hughes, Devito and Corton.

Déclaration de conflit d'intérêts

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The author(s) declared that they were an editorial board member of Frontiers, at the time of submission. This had no impact on the peer review process and the final decision.

Auteurs

Victoria Ledbetter (V)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.
Oak Ridge Associated Universities (ORAU), Oak Ridge, TN, United States.

Scott Auerbach (S)

National Institute of Environmental Health Sciences (NIEHS), Division of Translational Toxicology, Durham, NC, United States.

Logan J Everett (LJ)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.

Beena Vallanat (B)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.

Anna Lowit (A)

U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, United States.

Gregory Akerman (G)

U.S. Environmental Protection Agency, Office of Pesticide Programs, Washington, DC, United States.

William Gwinn (W)

National Institute of Environmental Health Sciences (NIEHS), Division of Translational Toxicology, Durham, NC, United States.

Leah C Wehmas (LC)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.

Michael F Hughes (MF)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.

Michael Devito (M)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.

J Christopher Corton (JC)

Center for Computational Toxicology and Exposure, US Environmental Protection Agency, Durham, NC, United States.

Classifications MeSH