Dertinger Stephen D, Totsuka Yukari, Bielas Jason H, Doherty Ann T, Kleinjans Jos, Honma Masamitsu, Marchetti Francesco, Schuler Maik J, Thybaud Veronique, White Paul, Yauk Carole L
Litron Laboratories, Rochester, NY, USA.
Division of Carcinogenesis and Cancer Prevention, National Cancer Center Research Institute, Tokyo, Japan.
Mutat Res Genet Toxicol Environ Mutagen. 2019 Nov;847:403022. doi: 10.1016/j.mrgentox.2019.02.003. Epub 2019 Feb 21.
We live in an era of 'big data', where the volume, velocity, and variety of the data being generated is increasingly influencing the way toxicological sciences are practiced. With this in mind, a workgroup was formed for the 2017 International Workshops on Genotoxicity Testing (IWGT) to consider the use of high information content data in genetic toxicology assessments. Presentations were given on adductomics, global transcriptional profiling, error-reduced single-molecule sequencing, and cellular phenotype-based assays, which were identified as methodologies that are relevant to present-day genetic toxicology assessments. Presenters and workgroup members discussed the state of the science for these methodologies, their potential use in genetic toxicology, current limitations, and the future work necessary to advance their utility and application. The session culminated with audience-assisted SWOT (strength, weakness, opportunities, and threats) analyses. The summary report described herein is structured similarly. A major conclusion of the workgroup is that while conventional regulatory genetic toxicology testing has served the public well over the last several decades, it does not provide the throughput that has become necessary in modern times, and it does not generate the mechanistic information that risk assessments ideally take into consideration. The high information content assay platforms that were discussed in this session, as well as others under development, have the potential to address aspect(s) of these issues and to meet new expectations in the field of genetic toxicology.
我们生活在一个“大数据”时代，所产生数据的数量、速度和种类日益影响着毒理学的实践方式。考虑到这一点，在2017年遗传毒性测试国际研讨会（IWGT）上成立了一个工作组，以探讨在遗传毒理学评估中使用高信息量数据的问题。会上介绍了加合物组学、全基因组转录谱分析、减少错误的单分子测序以及基于细胞表型的检测方法，这些方法被确定为与当今遗传毒理学评估相关的方法。报告人和工作组成员讨论了这些方法的科学现状、它们在遗传毒理学中的潜在用途、当前的局限性以及提高其效用和应用所需的未来工作。会议以观众协助的SWOT（优势、劣势、机会和威胁）分析告终。本文所述的总结报告结构与之类似。工作组的一个主要结论是，虽然传统的监管遗传毒性测试在过去几十年里为公众提供了很好的服务，但它无法提供现代所需的通量，也无法生成风险评估理想情况下应考虑的机制信息。本次会议讨论的高信息量检测平台以及其他正在开发的平台，有可能解决这些问题的某些方面，并满足遗传毒理学领域的新期望。
