Recent years have witnessed an expansion of mutagenesis research focusing on experimentally modeled genome-scale mutational signatures of carcinogens and of endogenous processes. Experimental mutational signatures can explain etiologic links to patterns found in human tumors that may be linked to same exposures, and can serve as biomarkers of exposure history and may even provide insights on causality. A number of innovative exposure models have been employed and reported, based on cells cultured in monolayers or in 3-D, on organoids, induced pluripotent stem cells, non-mammalian organisms, microorganisms and rodent bioassays. Here we discuss some of the latest developments and pros and cons of these experimental systems used in mutational signature analysis. Integrative designs that bring together multiple exposure systems (in vitro, in vivo and in silico pan-cancer data mining) started emerging as powerful tools to identify robust mutational signatures of the tested cancer risk agents. We further propose that devising a new generation of cell-based models is warranted to streamline systematic testing of carcinogen effects on the cell genomes, while seeking to increasingly supplant animal with non-animal systems to address relevant ethical issues and accentuate the 3R principles. We conclude that the knowledge accumulating from the growing body of signature modelling investigations has considerable power to advance cancer etiology studies and to support cancer prevention efforts through streamlined characterization of cancer-causing agents and the recognition of their specific effects.

近年来，诱变研究不断扩展，重点关注致癌物和内源过程的基因组规模突变特征的实验建模。实验突变特征可以解释人类肿瘤中发现的模式的病因学联系，这些模式可能与相同的暴露有关，并且可以作为暴露史的生物标志物，甚至可以提供对因果关系的见解。基于单层或 3D 培养的细胞、类器官、诱导多能干细胞、非哺乳动物生物体、微生物和啮齿动物生物测定，已经采用和报告了许多创新的暴露模型。在这里，我们讨论用于突变特征分析的这些实验系统的一些最新进展以及优缺点。将多个暴露系统（体外、体内和计算机泛癌数据挖掘）结合在一起的综合设计开始成为识别被测试癌症风险因素的强大突变特征的强大工具。我们进一步建议，有必要设计新一代基于细胞的模型，以简化致癌物对细胞基因组影响的系统测试，同时寻求越来越多地用非动物系统取代动物，以解决相关伦理问题并强调 3R 原则。我们的结论是，从越来越多的特征模型研究中积累的知识具有相当大的力量，可以推进癌症病因学研究，并通过简化致癌物质的表征和识别其特定作用来支持癌症预防工作。