National Center for Computational Toxicology, Office of Research and Development, US Environmental Protection Agency, Research Triangle Park, North Carolina 27711, USA.
Toxicol Sci. 2013 Jan;131(1):40-55. doi: 10.1093/toxsci/kfs285. Epub 2012 Sep 28.
Thousands of untested chemicals in the environment require efficient characterization of carcinogenic potential in humans. A proposed solution is rapid testing of chemicals using in vitro high-throughput screening (HTS) assays for targets in pathways linked to disease processes to build models for priority setting and further testing. We describe a model for predicting rodent carcinogenicity based on HTS data from 292 chemicals tested in 672 assays mapping to 455 genes. All data come from the EPA ToxCast project. The model was trained on a subset of 232 chemicals with in vivo rodent carcinogenicity data in the Toxicity Reference Database (ToxRefDB). Individual HTS assays strongly associated with rodent cancers in ToxRefDB were linked to genes, pathways, and hallmark processes documented to be involved in tumor biology and cancer progression. Rodent liver cancer endpoints were linked to well-documented pathways such as peroxisome proliferator-activated receptor signaling and TP53 and novel targets such as PDE5A and PLAUR. Cancer hallmark genes associated with rodent thyroid tumors were found to be linked to human thyroid tumors and autoimmune thyroid disease. A model was developed in which these genes/pathways function as hypothetical enhancers or promoters of rat thyroid tumors, acting secondary to the key initiating event of thyroid hormone disruption. A simple scoring function was generated to identify chemicals with significant in vitro evidence that was predictive of in vivo carcinogenicity in different rat tissues and organs. This scoring function was applied to an external test set of 33 compounds with carcinogenicity classifications from the EPA's Office of Pesticide Programs and successfully (p = 0.024) differentiated between chemicals classified as "possible"/"probable"/"likely" carcinogens and those designated as "not likely" or with "evidence of noncarcinogenicity." This model represents a chemical carcinogenicity prioritization tool supporting targeted testing and functional validation of cancer pathways.
环境中数以千计未经测试的化学物质需要对其在人类中的致癌潜力进行有效的特征描述。一种拟议的解决方案是使用体外高通量筛选（HTS）检测来快速检测化学物质，针对与疾病过程相关的通路中的靶标进行检测，从而为优先排序和进一步检测建立模型。我们描述了一种基于 292 种化学物质在 672 种测定中的 HTS 数据构建的预测啮齿动物致癌性的模型，这些测定映射到 455 个基因。所有数据均来自 EPA ToxCast 项目。该模型是基于毒性参考数据库（ToxRefDB）中具有体内啮齿动物致癌性数据的 232 种化学物质的子集进行训练的。与 ToxRefDB 中啮齿动物癌症相关的个别 HTS 测定与基因、通路和标志过程相关联，这些过程被记录为参与肿瘤生物学和癌症进展。啮齿动物肝癌终点与众所周知的途径（如过氧化物酶体增殖物激活受体信号传导和 TP53）以及 PDE5A 和 PLAUR 等新靶点相关联。与啮齿动物甲状腺肿瘤相关的癌症标志基因被发现与人类甲状腺肿瘤和自身免疫性甲状腺疾病相关联。开发了一种模型，其中这些基因/途径作为大鼠甲状腺肿瘤的假设增强子或启动子发挥作用，继发于甲状腺激素破坏的关键起始事件。生成了一个简单的评分函数，以识别具有显著体外证据的化学物质，这些证据可预测不同大鼠组织和器官中的体内致癌性。该评分函数应用于 EPA 农药计划办公室致癌性分类的 33 种化合物的外部测试集，并成功地（p = 0.024）区分了被归类为“可能”/“可能”/“可能”致癌剂的化学物质与被指定为“不太可能”或具有“非致癌性证据”的化学物质。该模型代表了一种化学致癌性优先级工具，支持癌症途径的靶向测试和功能验证。
