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Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens
Citation:
Hill, T., J. Rooney, J. Abedini, H. El-Masri, C. Wood, AND Jon Corton. Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens. TOXICOLOGICAL SCIENCES. Society of Toxicology, RESTON, VA, 177(1):41-59, (2020). https://doi.org/10.1093/toxsci/kfaa102
Impact/Purpose:
Dose thresholds have been used in toxicological science for many decades to set health guidance values for individual chemicals. Most commonly, these thresholds are based on the no/lowest observed adverse effect level (NOAEL/LOAEL) or benchmark dose (BMD), which is the calculated dose at which a statistically significant change beyond control is detected for a specific chemical treatment (Davis et al., 2011; Thomas et al., 2013). In contrast, an “effect” threshold based on biological response relationships can operate across chemicals within a particular model or system. It defines, for example, the level of receptor activation that may be needed for a mitogenic effect to occur. This concept is critical for pathway-based risk assessment because such chemical-agnostic hazard levels would enable predictive evaluations using an AOP construct. There are a number of examples of quantitative KE relationships within individual AOPs (e.g., (Wittwehr et al., 2017)). However, neither the concept of chemical-independent biological thresholds or its application to AOPs have been widely investigated within toxicological science for cancer risk assessment. Short-term changes in molecular profiles are a central component of strategies to rapidly identify and prospectively model health effects of environmental chemicals (Cote, et al., 2016; Meek et al., 2014; NRC, 2007; Thomas, et al., 2013). Use of higher-throughput data types should increase efficiency, support chemical screening and prioritization, reduce the number of animals used in testing, and allow for better allocation of resources to chemicals with the greatest potential risk. For cancer risk assessment, defining dose thresholds for known non-mutagenic carcinogens currently requires mode of action (MOA) studies that are resource-intensive and time-consuming (Boobis et al., 2006). There is a recognized need to streamline this process and bring newer types of molecular information into the cancer risk assessment process (Waters et al., 2010). According to the current 2005 EPA Cancer Guidelines (USEPA, 2005), the threshold dose for early key events in an accepted non-mutagenic tumorigenic mode of action (MOA) should be protective of the tumor outcome itself, based on the premise of dose and time concordance. In current practice, histopathological findings and cell proliferation from accepted MOAs are commonly used as a basis for determining the point of departure (POD) and reference dose (RfD). While quantitative genomic data corresponding to the initial early events in a target pathway are often submitted as part of the MOA/AOP evaluation, they are rarely if ever used as the basis for the RfD. Such quantitative genomic data is a promising information stream for use in the risk assessment process, provided reliable and reproducible biological thresholds for KEs can be identified that predestine a carcinogenic outcome. We hypothesized that biological thresholds based on expression patterns of genes specific to molecular initiating events (MIEs) in cancer AOPs might define chemical-agnostic thresholds for tumorigenesis and that these thresholds could be predictive for specific cancers. This hypothesis was tested in the context of rat liver cancer using a set of gene expression biomarkers for genotoxicity, aryl hydrocarbon receptor (AhR), constitutive activated receptor (CAR), estrogen receptor (ER), peroxisome proliferator-activated receptor α (PPARα), and cytotoxicity that were characterized in our previous work (Corton et al., 2019a; Rooney et al., 2018). In the present study, thresholds for the 6 biomarkers were determined using microarray data derived from large comprehensive studies of short-term chemical exposure.
Description:
Traditional methods for cancer risk assessment are resource-intensive, retrospective, and not feasible for the vast majority of environmental chemicals. In this study, we investigated whether quantitative genomic data from short-term studies may be used to set protective thresholds for potential tumorigenic effects. We hypothesized that gene expression biomarkers measuring activation of the key early events in established pathways for rodent liver cancer exhibit cross-chemical thresholds for tumorigenesis predictive for liver cancer risk. We defined biomarker thresholds for 6 major liver cancer pathways using training sets of chemicals with short-term genomic data (3–29 days of exposure) from the TG-GATES (n = 77 chemicals) and DrugMatrix (n = 86 chemicals) databases and then tested these thresholds within and between datasets. The 6 pathway biomarkers represented genotoxicity, cytotoxicity, and activation of xenobiotic, steroid, and lipid receptors (aryl hydrocarbon receptor, constitutive activated receptor, estrogen receptor, and peroxisome proliferator-activated receptor α). Thresholds were calculated as the maximum values derived from exposures without detectable liver tumor outcomes. We identified clear response values that were consistent across training and test sets. Thresholds derived from the TG-GATES training set were highly predictive (97%) in a test set of independent chemicals, whereas thresholds derived from the DrugMatrix study were 96%–97% predictive for the TG-GATES study. Threshold values derived from an abridged gene list (2/biomarker) also exhibited high predictive accuracy (91%–94%). These findings support the idea that early genomic changes can be used to establish threshold estimates or “molecular tipping points” that are predictive of later-life health outcomes.
URLs/Downloads:
DOI: Gene Expression Thresholds Derived From Short-term Exposures Identify Rat Liver Tumorigens
https://pubmed.ncbi.nlm.nih.gov/32603419/