A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays.

adverse outcome pathway aryl hydrocarbon receptor constitutive activated receptor cytotoxicity estrogen receptor genotoxicity liver cancer molecular initiating events peroxisome proliferator-activated receptor α transcript profiling

Journal

Toxicological sciences : an official journal of the Society of Toxicology
ISSN: 1096-0929
Titre abrégé: Toxicol Sci
Pays: United States
ID NLM: 9805461

Informations de publication

Date de publication:
01 09 2020
Historique:
pubmed: 1 7 2020
medline: 22 7 2021
entrez: 1 7 2020
Statut: ppublish

Résumé

Chemical-induced liver cancer occurs in rodents through well-characterized adverse outcome pathways. We hypothesized that measurement of the 6 most common molecular initiating events (MIEs) in liver cancer adverse outcome pathways in short-term assays using only gene expression will allow early identification of chemicals and their associated doses that are likely to be tumorigenic in the liver in 2-year bioassays. We tested this hypothesis using transcript data from a rat liver microarray compendium consisting of 2013 comparisons of 146 chemicals administered at doses with previously established effects on rat liver tumor induction. Five MIEs were measured using previously characterized gene expression biomarkers composed of gene sets predictive for genotoxicity and activation of 1 or more xenobiotic receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Because chronic injury can be important in tumorigenesis, we also developed a biomarker for cytotoxicity that had a 96% balanced accuracy. Characterization of the genes in each biomarker set using the unsupervised TXG-MAP network model demonstrated that the genes were associated with distinct functional coexpression modules. Using the Toxicological Priority Index to rank chemicals based on their ability to activate the MIEs showed that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Balanced accuracies using thresholds derived from either TG-GATES or DrugMatrix data sets to predict tumorigenicity in independent sets of chemicals were up to 93%. These results show that a MIE-directed approach using only gene expression biomarkers could be used in short-term assays to identify chemicals and their doses that cause tumors.

Identifiants

pubmed: 32603430
pii: 5865279
doi: 10.1093/toxsci/kfaa101
pmc: PMC8026143
mid: NIHMS1687284
doi:

Substances chimiques

Biomarkers 0

Types de publication

Journal Article Research Support, U.S. Gov't, Non-P.H.S.

Langues

eng

Sous-ensembles de citation

IM

Pagination

11-26

Subventions

Organisme : Intramural EPA
ID : EPA999999
Pays : United States

Informations de copyright

Published by Oxford University Press on behalf of the Society of Toxicology 2020. This work is written by US Government employees and is in the public domain in the US.

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Auteurs

J Christopher Corton (JC)

Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency (EPA), Research Triangle Park, North Carolina.

Thomas Hill (T)

Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency (EPA), Research Triangle Park, North Carolina.
Oak Ridge Institute for Science and Education (ORISE).

Jeffrey J Sutherland (JJ)

Indiana Biosciences Research Institute, Indianapolis, Indiana.

James L Stevens (JL)

Indiana Biosciences Research Institute, Indianapolis, Indiana.
Paradox Found LLC, Apex, North Carolina.

John Rooney (J)

Center for Computational Toxicology and Exposure, U.S. Environmental Protection Agency (EPA), Research Triangle Park, North Carolina.
Oak Ridge Institute for Science and Education (ORISE).
Integrated Lab Services, Research Triangle Park, NC 27560.

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