Clinical Pharmacology and Safety Sciences, R&D, AstraZeneca, Cambridge, UK.
Mutagenesis. 2020 Mar 27;35(2):153-159. doi: 10.1093/mutage/geaa007.
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.
毒理基因组学，即将基因组学应用于毒理学，被描述为毒理学的“新时代”。标准毒性测试通常涉及多项短期生物测定，这些测试既昂贵又耗时，需要大量动物，并且通常集中在单一终点上。毒理基因组学被吹捧为通过评估整个基因组的基因调控来提高毒性测试效率的一种方法，从而可以根据特征表达谱快速对化合物进行分类。基因表达微阵列可以在一项研究中测量和描述全基因组基因表达变化，虽然已经确定了可以区分遗传毒性和非遗传毒性致癌剂的转录组谱，但该方法的挑战限制了其应用。因此，毒理基因组学并没有像预期的那样改变遗传毒理学领域。最近，下一代测序（NGS）技术由于可以同时以比传统 Sanger 方法更便宜、更快的速度对数百亿个碱基对进行测序，从而彻底改变了基因组学。就遗传毒理学而言，已经对数千个癌症基因组进行了测序，并确定了单碱基替换突变特征，并且在用已知或疑似环境致癌物处理细胞后也确定了突变特征。RNAseq 已被应用于检测基因毒性处理后的转录变化；已经开发了修改的 RNAseq 协议来识别基因组中的加合物，并且 Duplex sequencing 是最近开发的一种技术，可用于准确检测突变。机器学习，包括 MutationSeq 和 SomaticSeq，也已被应用于体细胞突变检测，并且自动化的改进或机器学习算法的应用可能允许将来进行高通量突变测序。这篇综述将讨论转录组学在遗传毒理学中的最初前景，以及 NGS 技术和新的机器学习算法的发展如何最终实现这一前景。
