J. Christopher Corton

Humans are exposed to chemicals that leach from plastics, yet many remain data-poor and lack toxicological evaluation. High-throughput transcriptomics (HTTr) provides a scalable way to screen chemicals and generate mechanistic insights relevant to human health risk assessment. We applied HTTr in MCF-7 breast cancer cells to assess chemicals across several concentrations (0.001–50 µM) used in plastics and dyes. Transcriptomic points of departure (tPODs) were derived from general gene perturbations, including pathway-level and estrogen receptor α (ERα)–specific changes. We also used transcriptomic biomarkers to evaluate ERα activity and cellular stress responses. Most plastic chemicals showed similar toxicological potency, with tPODs falling within one order of magnitude. Bisphenol K was the most potent, activating ERα at the lowest concentrations, followed by plastic additive 08 and bisphenol A (BPA). Despite similar overall potency, transcriptomic biomarkers revealed distinct mechanisms. Chemicals structurally similar to BPA activated ERα, whereas others with different functional groups inhibited the ERα biomarker. Pathway and upstream regulator analyses further indicated that BPA-like chemicals consistently perturbed ERα-related pathways, while other chemicals enriched fewer gene sets and often produced opposite directional responses. At the highest concentrations tested, several chemicals also activated stress-response biomarkers and suppressed proliferation. Overall, these results suggest that while many plastic chemicals exhibit comparable in vitro potency, they diverge in ERα regulation and downstream biological pathways. This study demonstrates the reproducibility and value of HTTr for chemical screening, supports grouping ERα-active plastic chemicals for read-across, and underscores the need for additional evaluation of plastic chemicals.

While many jurisdictions have phased out use of bisphenol A (BPA), there is increasing exposure to mixtures of BPA alternatives. Like BPA, some alternatives perturb nuclear hormone receptors and are endocrine disruptors. We used high-throughput transcriptomics (HTTr) to evaluate the potency and modes of action of seven mixtures of BPA alternatives and their 12 individual components in breast cancer cells. Our aim was to explore whether alternatives present in mixtures act additively. MCF-7 cells were exposed to chemicals (0.001–50 µM) for 48 h and gene expression analysis was used to measure global and estrogen receptor alpha (ERα)-specific transcriptomic changes. Transcriptomic points of departure (tPODs) were derived using benchmark concentration (BMC) modelling. We first identified concentrations at which global transcriptional activity was robustly altered. Then, we applied a ERα transcriptomic biomarker to identify ERα agonists and predict ERα activation tPODs. We employed mixtures modeling to predict potency of BPA alternatives and test for additive effects in vitro. Ingenuity pathway analysis (IPA; Qiagen) was used to identify upstream regulators and canonical pathways from genes fitting BMCs. BPAF was the most potent individual chemical tested overall, followed by BPA and BPC. All seven mixtures had additive effects across all tPODs modeled. The ERα transcriptomic biomarker classified all mixtures as ERα activators along with several BPA alternatives. All mixtures and most individual components perturbed similar upstream regulators and pathways, suggesting common modes of action. These data support the value of HTTr in identifying additive effects and toxicological potency of mixtures in vitro.

The polyfluorinated alkyl substance (PFAS) Nafion BP2 (1,1,2,2-tetrafluoro-2-[1,1,1,2,3,3-hexafluoro-3-(1,1,2,2-tetrafluoroethoxy)propan-2-yl]oxyethane-1-sulfonic acid) has been detected in surface and ground water, as well as in the blood of people living near PFAS manufacturing facilities. Given that very little is known about the potential toxicity of Nafion BP2 and safe exposure levels have not yet been determined, we performed a benchmark dose analysis of phenotypic and genomic effects in mice. Male and female Balb-c mice were exposed daily to Nafion BP2 at multiple doses for 7 d by oral gavage. Full-genome transcript profiling showed that Nafion BP2 in both sexes activates a number of transcription factors linked to liver toxicity, including constitutive androstane receptor (CAR), pregnane X receptor, and NRF2, but unlike other long-chain PFAS, there was no activation of peroxisome proliferator-activated receptor α. Nafion BP2 caused hepatic steatosis in both sexes. Benchmark dose (BMD) estimates for 15 non-genomic effects were 0.25 mg/kg/d and above. BMDs for transcriptional effects were 0.04 mg/kg/d and above. The most sensitive gene sets in both males and females were related to effects on xenobiotic metabolism and the cell cycle, which are plausibly related at a mechanistic level to the activation of CAR. The xenobiotic metabolism and cell cycle findings were largely consistent when dose values based on internal dose were employed in the analysis. These values, along with the identification of molecular targets linked to hazards, may facilitate the determination of human health guidance for Nafion BP2.

The current genotoxicity testing paradigm provides little mechanistic information, has poor specificity in predicting carcinogenicity in humans, and is not suited to assessing a large number of chemicals. Genomic technologies enable the characterization of genome‐wide transcriptional changes in response to chemical treatments that can inform mechanisms or modes of action. These technologies provided an impetus to develop transcriptomic biomarkers that could transform genotoxicity hazard assessment for drugs, cosmetics, and environmental and industrial chemicals. In August 2022, the International Workshops on Genotoxicity Testing (IWGT) held a workshop to critically review progress in the development and application of transcriptomic biomarkers in genotoxicity testing. Here, we describe the findings of this workshop's subgroup that conducted a systematized review and analysis of in vitro transcriptomic biomarkers for evaluating genotoxicity. Although there is a multitude of published reports exploring transcriptomics in genetic toxicology, the working group identified only five in vitro transcriptomic biomarker candidates, of which three (GENOMARK, TGx‐DDI, and MU2012) were independently developed with sufficiently defined context of use, validation data, and supporting case studies that warranted inclusion in the review. Although these in vitro biomarkers were developed independently and for different classes of chemicals (TGx‐DDI for pharmaceuticals, GENOMARK for cosmetics, and MU2012 for medical and environmental chemicals), they all address the same shortfall of the standard in vitro genotoxicity testing battery, that is, lack of specificity by genotoxicity‐induced stress response at the transcriptomic level. In this review, we discuss the development of these in vitro biomarkers, including challenges and progress toward achieving regulatory acceptance.

There is growing recognition across broad sectors of the toxicology community that gene expression biomarkers have the potential to identify genotoxic and nongenotoxic carcinogens through a weight‐of‐evidence approach, providing opportunities to reduce reliance on the 2‐year bioassay to identify carcinogens. In August 2022, a workshop within the International Workshops on Genotoxicity Testing (IWGT) was held to critically review current methods to identify genotoxicants using various ‘omics profiling methods. Here, we describe the findings of a workshop subgroup focused on the state of the science regarding the use of biomarkers to identify chemicals that act as genotoxicants in vivo. A total of 1341 papers were screened to identify those that were most relevant. While six published biomarkers with characterized accuracy were initially examined, four of the six were not considered further, because they had not been tested for classification accuracy using additional sets of chemicals or other transcript profiling platforms. Two independently derived biomarkers used in conjunction with standard computational techniques can identify genotoxic chemicals in vivo (rat liver or both rat and mouse liver) on different gene expression profiling platforms. The biomarkers have predictive accuracies of ≥92%. These biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies using short‐term rodent exposures to identify genotoxic and nongenotoxic chemicals that cause cancer.

Gene expression biomarkers have the potential to identify genotoxic and non‐genotoxic carcinogens, providing opportunities for integrated testing and reducing animal use. In August 2022, an International Workshops on Genotoxicity Testing (IWGT) workshop was held to critically review current methods to identify genotoxicants using transcriptomic profiling. Here, we summarize the findings of the workgroup on the state of the science regarding the use of transcriptomic biomarkers to identify genotoxic chemicals in vitro and in vivo. A total of 1341 papers were examined to identify the biomarkers that show the most promise for identifying genotoxicants. This analysis revealed two independently derived in vivo biomarkers and three in vitro biomarkers that, when used in conjunction with standard computational techniques, can identify genotoxic chemicals in vivo (rat or mouse liver) or in human cells in culture using different gene expression profiling platforms, with predictive accuracies of ≥92%. These biomarkers have been validated to differing degrees but typically show high reproducibility across transcriptomic platforms and model systems. They offer several advantages for applications in different contexts of use in genotoxicity testing including: early signal detection, moderate‐to‐high‐throughput screening capacity, adaptability to different cell types and tissues, and insights on mechanistic information on DNA‐damage response. Workshop participants agreed on consensus statements to advance the regulatory adoption of transcriptomic biomarkers for genotoxicity. The participants agreed that transcriptomic biomarkers have the potential to be used in conjunction with other biomarkers in integrated test strategies in vitro and using short‐term rodent exposures to identify genotoxic and non‐genotoxic chemicals that may cause cancer and heritable genetic effects. Following are the consensus statements from the workgroup. Transcriptomic biomarkers for genotoxicity can be used in Weight of Evidence (WoE) evaluation to: determine potential genotoxic mechanisms and hazards; identify misleading positives from in vitro genotoxicity assays; serve as new approach methodologies (NAMs) integrated into the standard battery of genotoxicity tests. Several transcriptomic biomarkers have been developed from sufficiently robust training data sets, validated with external test sets, and have demonstrated performance in multiple laboratories. These transcriptomic biomarkers can be used following established study designs and models designated through existing validation exercises in WoE evaluation. Bridging studies using a selection of training and test chemicals are needed to deviate from the established protocols to confirm performance when a transcriptomic biomarker is being applied in other: tissues, cell models, or gene expression platforms. Top dose selection and time of gene expression analysis are critical and should be established during transcriptomic biomarker development. These conditions are the only ones suited for transcriptomic biomarker use unless additional bridging or pharmacokinetic studies are conducted. Temporal effects for genotoxicants that operate via distinct mechanisms should be considered in data interpretation. Fixed transcriptomic biomarker gene sets and analytical processes do not need to be independently rederived in biomarker validation. Validation should focus on the performance of the gene set in external test sets. Robust external testing should ensure a minimum of additional chemicals spanning genotoxic and non‐genotoxic modes of action. Genes in the transcriptomic biomarker do not need to be known to be mechanistically involved in genotoxicity responses. Existing frameworks described for NAMs could be applied for validation of transcriptomic biomarkers. Reproducibility of bioinformatic analysis is critical for the regulatory application of transcriptomic biomarkers. A bioinformatics expert should be involved with creating reproducible methods for the qualification and application of each transcriptomic biomarker.

As a part of the International Workshop on Genotoxicity Testing (IWGT) in 2022, a workgroup was formed to evaluate the level of validation and regulatory acceptance of transcriptomic biomarkers that identify genotoxic substances. Several such biomarkers have been developed using various molecular techniques and computational approaches. Within the IWGT workgroup on transcriptomic biomarkers, bioinformatics was a central topic of discussion, focusing on the current approaches used to process the underlying molecular data to distill a reliable predictive signal; that is, a gene set that is indicative of genotoxicity and can then be used in later studies to predict potential DNA damaging properties for uncharacterized chemicals. While early studies used microarray data, a technological shift occurred in the past decade to incorporate modern transcriptome measuring techniques such as high‐throughput transcriptomics, which in turn is based on high‐throughput sequencing. Herein, we present the workgroup's review of the current bioinformatic approaches to identify genes comprising transcriptomic biomarkers. Within the context of regulatory toxicology, the reproducibility of a given analysis is critical. Therefore, the workgroup provides consensus recommendations on how to facilitate sufficient reporting of experimental parameters for the analytical procedures used in a transcriptomic biomarker study, including the recommendation to develop a biomarker‐specific reporting module within the OECD Omics Reporting Framework.

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.