Using integrative informatics to bridge toxicogenomics and disease biology
Joel T. Dudley · ISEE Conference Abstracts · 2013
Traditional epidemiological methods for associating environmental exposures to human health are often limited in their ability to uncover molecular mechanisms that link molecular perturbations induced by a chemical exposure to pathophysiology. Additionally, traditional methods based on investigating exposed populations provide limited opportunities to construct predictive toxicogenomic models capable of identifying health liabilities of chemical exposures prior to observing manifestations of their potential deleterious effects in a population. The goal of this study is to develop and apply integrative informatics methodologies that incorporate existing molecular data from human diseases and chemical perturbagens to identify novel links between chemical exposures and disease pathophysiology. We developed a novel method that integrates disease molecular profiles from the NCBI Gene Expression Omnibus (GEO) with molecular signatures of chemical exposures constructed from representative data in the Comparative Toxicogenomics Database (CTD). This method employs a statistical approach to identify synergistic molecular patterns overlapping between disease states and chemical exposures, which suggest that an exposure could induce or exacerbate pathological molecular states in human tissues. We applied this method to multiple molecular profiles of breast cancer to identify chemical exposures that induce molecular patterns that are significantly synergistic with multiple representations of the disease state. Our analysis identified many chemical agents, such as Polychlorinated biphenyls (PCBs), which are known to modulate the pathophysiology of breast cancer. This study demonstrates the utility and potential for developing and applying integrative informatics approaches to enable predictive toxicogenomics and to identify putative molecular mechanisms underlying associations between chemical exposures and human disease states.