A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-term Assays. (30th June 2020)

• Record Type: Journal Article
• Authors: Corton, J Christopher; Hill, Thomas; Sutherland, Jeffrey J; Stevens, James L; Rooney, John
• Is Part Of: Toxicological sciences. Volume 177:Number 1(2020)
• Page Start: 11
• Page End: 26
• Publication Date: 2020-06-30
• Subjects: adverse outcome pathway -- constitutive activated receptor -- transcript profiling -- liver cancer -- peroxisome proliferator-activated receptor α -- aryl hydrocarbon receptor -- genotoxicity -- estrogen receptor -- cytotoxicity -- molecular initiating events; Toxicology -- Periodicals; Toxicology -- Periodicals; Toxicology; Periodicals; 615.9
• DOI: 10.1093/toxsci/kfaa101
• Languages: English
• ISSNs: 1096-6080

Abstract

Abstract: 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.