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BioMed Research International
Volume 2015 (2015), Article ID 515798, 11 pages
Review Article

Applying a Weight-of-Evidence Approach to Evaluate Relevance of Molecular Landscapes in the Exposure-Disease Paradigm

1Cardno ChemRisk, 4840 Pearl East Circle 300 W., Boulder, CO 80304, USA
2Department of Environmental & Radiological Health Sciences, Colorado State University, Fort Collins, CO 80523, USA

Received 20 December 2014; Accepted 16 March 2015

Academic Editor: Jia Cao

Copyright © 2015 Sherilyn A. Gross and Kristen M. Fedak. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Information on polymorphisms, mutations, and epigenetic events has become increasingly important in our understanding of molecular mechanisms associated with exposures-disease outcomes. Molecular landscapes can be developed to illustrate the molecular characteristics for environmental carcinogens as well as associated disease outcomes, although comparison of these molecular landscapes can often be difficult to navigate. We developed a method to organize these molecular data that uses a weight-of-evidence approach to rank overlapping molecular events by relative importance for susceptibility to an exposure-disease paradigm. To illustrate the usefulness of this approach, we discuss the example of benzene as an environmental carcinogen and myelodysplastic syndrome (MDS) as a causative disease endpoint. Using this weight-of-evidence method, we found overlapping polymorphisms in the genes for the metabolic enzymes GST and NQO1, both of which may infer risk of benzene-induced MDS. Polymorphisms in the tumor suppressor gene, TP53, and the inflammatory cytokine gene, TNF-α, were also noted, albeit inferring opposing outcomes. The alleles identified in the DNA repair gene RAD51 indicated an increased risk for MDS in MDS patients and low blood cell counts in benzene-exposed workers. We propose the weight-of-evidence approach as a tool to assist in organizing the sea of emerging molecular data in exposure-disease paradigms.