Unlocking the Power of Transcriptomic Biomarkers in Qualitative and Quantitative Genotoxicity Assessment of Chemicals.

To modernize genotoxicity assessment and reduce reliance on experimental animals, new approach methodologies (NAMs) that provide human-relevant dose-response data are needed. Two transcriptomic biomarkers, GENOMARK and TGx-DDI, have shown a high classification accuracy for genotoxicity. As these biomarkers were extracted from different training sets, we investigated whether combining the two biomarkers in a human-derived metabolically competent cell line (i.e., HepaRG) provides complementary information for the classification of genotoxic hazard identification and potency ranking. First, the applicability of GENOMARK to TempO-Seq, a high-throughput transcriptomic technology, was evaluated. HepaRG cells were exposed for 72 h to increasing concentrations of 10 chemicals (i.e., eight known in vivo genotoxicants and two in vivo nongenotoxicants). Gene expression data were generated using the TempO-Seq technology. We found a prediction performance of 100%, confirming the applicability of GENOMARK to TempO-Seq. Classification using TGx-DDI was then compared to GENOMARK. For the chemicals identified as genotoxic, benchmark concentration modeling was conducted to perform potency ranking. The high concordance observed for both hazard classification and potency ranking by GENOMARK and TGx-DDI highlights the value of integrating these NAMs in a weight of evidence evaluation of genotoxicity.

Impact of experimental design factors on the potency of genotoxicants in in vitro tests.

Previous studies have shown that differences in experimental design factors may alter the potency of genotoxic compounds in in vitro genotoxicity tests. Most of these studies used traditional statistical methods based on the lowest observed genotoxic effect levels, whereas more appropriate methods, such as the benchmark dose (BMD) approach, are now available to compare genotoxic potencies under different test conditions. We therefore investigated the influence of two parameters, i.e. cell type and exposure duration, on the potencies of two known genotoxicants [aflatoxin B1 and ethyl methanesulfonate (EMS)] in the in vitro micronucleus (MN) assay and comet assay (CA). Both compounds were tested in the two assays using two cell types (i.e. CHO-K1 and TK6 cells). To evaluate the effect of exposure duration, the genotoxicity of EMS was assessed after 3 and 24 h of exposure. Results were analyzed using the BMD covariate approach, also referred to as BMD potency ranking, and the outcome was compared with that of more traditional statistical methods based on lowest observed genotoxic effect levels. When comparing the in vitro MN results obtained in both cell lines with the BMD covariate approach, a difference in potency was detected only when EMS exposures were conducted for 24 h, with TK6 cells being more sensitive. No difference was observed in the potency of both EMS and aflatoxin B1 in the in vitro CA using both cell lines. In contrast, EMS was more potent after 24 h exposure compared with a 3 h exposure under all tested conditions, i.e. in the in vitro MN assay and CA in both cell lines. Importantly, for several of the investigated factors, the BMD covariate method could not be used to confirm the differences in potencies detected with the traditional statistical methods, thus highlighting the need to evaluate the impact of experimental design factors with adequate approaches.

Novel prediction models for genotoxicity based on biomarker genes in human HepaRG™ cells.

Transcriptomics-based biomarkers are promising new approach methodologies (NAMs) to identify molecular events underlying the genotoxic mode of action of chemicals. Previously, we developed the GENOMARK biomarker, consisting of 84 genes selected based on whole genomics DNA microarray profiles of 24 (non-)genotoxic reference chemicals covering different modes of action in metabolically competent human HepaRG™ cells. In the present study, new prediction models for genotoxicity were developed based on an extended reference dataset of 38 chemicals including existing as well as newly generated gene expression data. Both unsupervised and supervised machine learning algorithms were used, but as unsupervised machine learning did not clearly distinguish between groups, the performance of two supervised machine learning algorithms, i.e., support vector machine (SVM) and random forest (RF), was evaluated. More specifically, the predictive accuracy was compared, the sensitivity to outliers for one or more biomarker genes was assessed, and the prediction performance for 10 misleading positive chemicals exposed at their IC10 concentration was determined. In addition, the applicability of both prediction models on a publicly available gene expression dataset, generated with RNA-sequencing, was investigated. Overall, the RF and SVM models were complementary in their classification of chemicals for genotoxicity. To facilitate data analysis, an online application was developed, combining the outcomes of both prediction models. This research demonstrates that the combination of gene expression data with supervised machine learning algorithms can contribute to the ongoing paradigm shift towards a more human-relevant in vitro genotoxicity testing strategy without the use of experimental animals.