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

Anouck Thienpont, Eunnara Cho, Andrew Williams, Matthew J. Meier, Carole L. Yauk, Vera Rogiers, Tamara Vanhaecke, Birgit Mertens

2024Chemical Research in Toxicology18 citationsDOIOpen Access PDF

Abstract

High Resolution Image Download MS PowerPoint Slide 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.

Topics & Concepts

GenotoxicityComputational biologyTranscriptomeChemistryBiologyData scienceComputer scienceGeneticsToxicityGene expressionGeneOrganic chemistryCarcinogens and Genotoxicity AssessmentMolecular Biology Techniques and ApplicationsGene expression and cancer classification