Department of Veterinary Pharmacology and Physiology, Texas A&M University, College Station, Texas 77843, USA.
Department of Statistics, Bioinformatics Research Center, North Carolina State University, Raleigh, North Carolina 27606, USA.
Toxicol Sci. 2024 Feb 28;198(1):141-154. doi: 10.1093/toxsci/kfad134.
Systematic review and evaluation of mechanistic evidence using the Key Characteristics approach was proposed by the International Agency for Research on Cancer (IARC) in 2012 and used by the IARC Monographs Working Groups since 2015. Key Characteristics are 10 features of agents known to cause cancer in humans. From 2015 to 2022, a total of 19 Monographs (73 agents combined) used Key Characteristics for cancer hazard classification. We hypothesized that a retrospective analysis of applications of the Key Characteristics approach to cancer hazard classification using heterogenous mechanistic data on diverse agents would be informative for systematic reviews in decision-making. We extracted information on the conclusions, data types, and the role mechanistic data played in the cancer hazard classification from each Monograph. Statistical analyses identified patterns in the use of Key Characteristics, as well as trends and correlations among Key Characteristics, data types, and ultimate decisions. Despite gaps in data for many agents and Key Characteristics, several significant results emerged. Mechanistic data from in vivo animal, in vitro animal, and in vitro human studies were most impactful in concluding that an agent could cause cancer via a Key Characteristic. To exclude the involvement of a Key Characteristic, data from large-scale systematic in vitro testing programs such as ToxCast, were most informative. Overall, increased availability of systemized data streams, such as human in vitro data, would provide the basis for more confident and informed conclusions about both positive and negative associations and inform expert judgments on cancer hazard.
国际癌症研究机构(IARC)于 2012 年提出了使用关键特征方法进行系统评价和机制证据评估,并自 2015 年以来被 IARC 专论工作组使用。关键特征是 10 种已知可导致人类癌症的物质的特征。2015 年至 2022 年,共有 19 项专论(73 种物质组合)使用关键特征对致癌危害进行分类。我们假设,对使用不同机制数据对不同物质进行致癌危害分类的关键特征方法的应用进行回顾性分析,将为决策中的系统评价提供信息。我们从每篇专论中提取了有关结论、数据类型以及机制数据在致癌危害分类中的作用的信息。统计分析确定了关键特征应用的模式,以及关键特征、数据类型和最终决策之间的趋势和相关性。尽管许多物质和关键特征的数据存在差距,但仍出现了一些重要结果。来自体内动物、体外动物和体外人类研究的机制数据在得出物质可以通过关键特征致癌的结论方面最具影响力。为了排除关键特征的参与,来自大规模系统体外测试计划(如 ToxCast)的数据最具信息性。总体而言,系统数据流(如人类体外数据)的可用性增加将为正面和负面关联提供更有信心和信息丰富的结论,并为癌症危害的专家判断提供依据。