Römer Michael, Eichner Johannes, Metzger Ute, Templin Markus F, Plummer Simon, Ellinger-Ziegelbauer Heidrun, Zell Andreas
Center of Bioinformatics Tuebingen (ZBIT), University of Tuebingen, Tübingen, Germany.
Natural and Medical Sciences Institute at the University of Tübingen, Reutlingen, Germany.
PLoS One. 2014 May 15;9(5):e97640. doi: 10.1371/journal.pone.0097640. eCollection 2014.
In the area of omics profiling in toxicology, i.e. toxicogenomics, characteristic molecular profiles have previously been incorporated into prediction models for early assessment of a carcinogenic potential and mechanism-based classification of compounds. Traditionally, the biomarker signatures used for model construction were derived from individual high-throughput techniques, such as microarrays designed for monitoring global mRNA expression. In this study, we built predictive models by integrating omics data across complementary microarray platforms and introduced new concepts for modeling of pathway alterations and molecular interactions between multiple biological layers. We trained and evaluated diverse machine learning-based models, differing in the incorporated features and learning algorithms on a cross-omics dataset encompassing mRNA, miRNA, and protein expression profiles obtained from rat liver samples treated with a heterogeneous set of substances. Most of these compounds could be unambiguously classified as genotoxic carcinogens, non-genotoxic carcinogens, or non-hepatocarcinogens based on evidence from published studies. Since mixed characteristics were reported for the compounds Cyproterone acetate, Thioacetamide, and Wy-14643, we reclassified these compounds as either genotoxic or non-genotoxic carcinogens based on their molecular profiles. Evaluating our toxicogenomics models in a repeated external cross-validation procedure, we demonstrated that the prediction accuracy of our models could be increased by joining the biomarker signatures across multiple biological layers and by adding complex features derived from cross-platform integration of the omics data. Furthermore, we found that adding these features resulted in a better separation of the compound classes and a more confident reclassification of the three undefined compounds as non-genotoxic carcinogens.
在毒理学的组学分析领域，即毒理基因组学中，特征性分子谱先前已被纳入预测模型，用于化合物致癌潜力的早期评估和基于机制的分类。传统上，用于模型构建的生物标志物特征来自个体高通量技术，例如用于监测全局mRNA表达的微阵列。在本研究中，我们通过整合互补微阵列平台的组学数据构建了预测模型，并引入了用于多生物层间通路改变和分子相互作用建模的新概念。我们在一个跨组学数据集上训练和评估了多种基于机器学习的模型，这些模型在纳入的特征和学习算法上有所不同，该数据集包含从用一组异质物质处理的大鼠肝脏样本中获得的mRNA、miRNA和蛋白质表达谱。根据已发表研究的证据，这些化合物中的大多数可明确分类为遗传毒性致癌物、非遗传毒性致癌物或非肝癌致癌物。由于已报道醋酸环丙孕酮、硫代乙酰胺和Wy-14643具有混合特征，我们根据它们的分子谱将这些化合物重新分类为遗传毒性或非遗传毒性致癌物。在重复的外部交叉验证程序中评估我们的毒理基因组学模型，我们证明通过整合多个生物层的生物标志物特征以及添加从组学数据的跨平台整合中衍生的复杂特征，可以提高我们模型的预测准确性。此外，我们发现添加这些特征可更好地分离化合物类别，并更有信心地将三种未定义化合物重新分类为非遗传毒性致癌物。
