Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States.
Center for Computational Toxicology and Exposure, U.S. EPA, Research Triangle Park, NC, United States; Oak Ridge Institute for Science and Education (ORISE) fellow Office of Research and Development, U.S. Environmental Protection Agency (EPA), Research Triangle Park, NC, United States.
Toxicology. 2020 Oct;443:152547. doi: 10.1016/j.tox.2020.152547. Epub 2020 Aug 2.
Traditional methods for cancer risk assessment are retrospective, resource-intensive, and not feasible for the vast majority of environmental chemicals. In earlier studies, we used a set of six biomarkers to accurately identify liver tumorigens in transcript profiles derived from chemically-treated rats using either a Toxicological Priority Index (ToxPi) approach or using derived biomarker thresholds for cancer. The biomarkers consisting of 7-113 genes are used to predict the most common liver cancer molecular initiating events: genotoxicity, cytotoxicity and activation of the xenobiotic receptors AhR, CAR, ER, and PPARα. In the present study, we apply and evaluate the performance of these methods for cancer prediction in an independent rat liver study of 44 chemicals (6 h-7d exposures) examined by Affymetrix arrays. In the first approach, ToxPi ranking of biomarker scores consistently gave the highest scores to tumorigenic chemical-dose pairs; balanced accuracies for identification of liver tumorigenic chemicals were up to 89 %. The second approach used tumorigenic thresholds derived in the present study or from our earlier study that were set at the maximum value for chemical-dose exposures without detectable liver tumor outcomes. Using these thresholds, balanced accuracies were up to 90 %. Both approaches identified all tumorigenic chemicals. Almost all of the tumorigenic chemicals activated more than one MIE. We also compared biomarker responses between two types of profiling platforms (Affymetrix full-genome array, TempO-Seq 1500+ array containing ∼2600 genes) and found that the lack of the full set of biomarker genes on the 1500+ array resulted in decreased ability to identify chemicals that activate the MIEs. Overall, these results demonstrate that predictive approaches based on the 6 biomarkers could be used in short-term assays to identify chemicals and their doses that induce liver tumors, the most common endpoint in rodent bioassays.
传统的癌症风险评估方法是回顾性的,资源密集型的,并且对于绝大多数环境化学物质来说是不可行的。在早期的研究中,我们使用了一组六个生物标志物,通过使用毒性优先指数(ToxPi)方法或使用癌症衍生的生物标志物阈值,从化学处理过的大鼠的转录谱中准确识别肝肿瘤形成物。由 7-113 个基因组成的生物标志物用于预测最常见的肝癌分子起始事件:遗传毒性、细胞毒性和外源物受体 AhR、CAR、ER 和 PPARα 的激活。在本研究中,我们应用并评估了这些方法在 44 种化学物质(6 h-7d 暴露)的独立大鼠肝研究中的性能,这些化学物质通过 Affymetrix 阵列进行了检查。在第一种方法中,生物标志物评分的 ToxPi 排序始终为肿瘤形成性化学剂量对提供最高评分;识别肝肿瘤形成性化学物质的平衡准确率高达 89%。第二种方法使用了本研究或我们早期研究中得出的肿瘤形成性阈值,这些阈值设定在没有可检测到的肝肿瘤结果的化学剂量暴露的最大值。使用这些阈值,平衡准确率高达 90%。这两种方法都识别出了所有的肿瘤形成性化学物质。几乎所有的肿瘤形成性化学物质都激活了不止一种 MIE。我们还比较了两种类型的基因表达谱平台(Affymetrix 全基因组阵列、包含约 2600 个基因的 TempO-Seq 1500+ 阵列)之间的生物标志物反应,发现 1500+ 阵列上缺乏全套生物标志物基因会降低识别激活 MIE 的化学物质的能力。总的来说,这些结果表明,基于 6 个生物标志物的预测方法可以在短期试验中用于识别诱导肝脏肿瘤的化学物质及其剂量,肝脏肿瘤是啮齿动物生物测定中最常见的终点。