Abstract
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.