Citation:

Corton, Jon, T. Hill, J. Sutherland, J. Stevens, AND J. Rooney. A Set of Six Gene Expression Biomarkers Identify Rat Liver Tumorigens in Short-Term Assays. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 177(1):11-26, (2020). https://doi.org/10.1093/toxsci/kfaa101

Impact/Purpose:

We have recently taken an adverse outcome pathway (AOP)-based approach to predicting rat liver cancer [18], the most common target organ for cancer in the rodent [19, 20]. Pathway information organized by mode of action (MOA) [21, 22] or more recently, by an AOP construct [23, 24] plays a central role in compiling evidence of effects and determining human relevance for chemical carcinogens. A chemical-agnostic AOP starts with the interaction between a chemical and a molecular target (the molecular initiating event (MIE) followed by a series of downstream key events (KEs) that lead to an adverse outcome (AO). Overlay of chemical-specific information such as absorption, disposition, metabolism and excretion (ADME) and prediction of chemical concentrations at the site of the MIE permits the derivation of the corresponding MOA for risk assessment [23]. In our earlier study, we hypothesized that measurement of the MIEs and KEs for rat liver cancer AOPs in short-term assays would allow early identification of chemicals, and their associated doses, that are likely to be tumorigenic in the liver in two-year bioassays. That hypothesis was predicated on the fact that while a number of mechanisms that lead to liver cancer have been described or hypothesized [17], most chemicals cause rodent liver cancer through only 6 major pathways. The MIEs for these include DNA damage, cytotoxicity, and regenerative cell proliferation, and activation of one or more xenobiotic receptors (aryl hydrocarbon receptor (AhR), constitutive activated receptor (CAR), estrogen receptor (ER), and peroxisome proliferator-activated receptor α (PPARα)) (Figure 1). These MIEs and KEs were measured using a combination of gene expression biomarkers characterized as part of the study (AhR, CAR, ER, genotoxicity, PPARα), expression of individual genes (indirect measures of oxidative stress), clinical chemistry measurements (ALT and AST to measure cytotoxicity), and liver to body weights (an indirect measure of hepatocyte proliferation and hypertrophy). We employed the Toxicological Priority Index (ToxPi) to rank chemicals based on their ability to activate MIEs/KEs, and found that chemicals administered at tumorigenic doses clearly gave the highest ranked scores. Depending on the time selected to make predictions, the accuracy ranged from 85-89% and was highest when endpoints were measured at 4d. We have refined our earlier hypothesis and in this study, we now propose that methods using only gene expression biomarkers could be used to accurately and precisely identify liver tumorigens in short-term assays. We tested our hypothesis using the microarray data mined from the TG-GATEs study [18] [25] and the DrugMatrix study [12] both of which examine a large cohort of chemicals at multiple doses and times of exposure in the rat. Using a ToxPi analysis, we found that our set of 6 biomarkers displayed excellent predictive performance metrics.

Description:

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.

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