The promise of toxicogenomics for genetic toxicology: past, present and future

Abstract

Abstract Toxicogenomics, the application of genomics to toxicology, was described as ‘a new era’ for toxicology. Standard toxicity tests typically involve a number of short-term bioassays that are costly, time consuming, require large numbers of animals and generally focus on a single end point. Toxicogenomics was heralded as a way to improve the efficiency of toxicity testing by assessing gene regulation across the genome, allowing rapid classification of compounds based on characteristic expression profiles. Gene expression microarrays could measure and characterise genome-wide gene expression changes in a single study and while transcriptomic profiles that can discriminate between genotoxic and non-genotoxic carcinogens have been identified, challenges with the approach limited its application. As such, toxicogenomics did not transform the field of genetic toxicology in the way it was predicted. More recently, next generation sequencing (NGS) technologies have revolutionised genomics owing to the fact that hundreds of billions of base pairs can be sequenced simultaneously cheaper and quicker than traditional Sanger methods. In relation to genetic toxicology, and thousands of cancer genomes have been sequenced with single-base substitution mutational signatures identified, and mutation signatures have been identified following treatment of cells with known or suspected environmental carcinogens. RNAseq has been applied to detect transcriptional changes following treatment with genotoxins; modified RNAseq protocols have been developed to identify adducts in the genome and Duplex sequencing is an example of a technique that has recently been developed to accurately detect mutation. Machine learning, including MutationSeq and SomaticSeq, has also been applied to somatic mutation detection and improvements in automation and/or the application of machine learning algorithms may allow high-throughput mutation sequencing in the future. This review will discuss the initial promise of transcriptomics for genetic toxicology, and how the development of NGS technologies and new machine learning algorithms may finally realise that promise.