Abstract 5224: Breast cancer biomarkers from biosimulations: transcriptome-to-reactome™ technology.
Authors: LeBaron Richard , Villareal Greg , Roberson Dawnlee J. , Phelix Clyde 
Published: August 2014

Genome-wide gene-expression profiles, transcriptomes, from microarray tests are used for clinical decision-making; novel uses of transcriptomes will continue to emerge. We are commercializing a patent-pending technology using transcriptomes to determine parameters for deterministic-kinetic models of biological pathways, Transcriptome-To-Reactome:Transforming Growth Factor Beta Signaling. We demonstrate how biosimulation results for proteomics, flux, and a derivative property of the network system can be utilized as biomarkers for breast cancer. The TGFbeta Signaling Model was accessed from Reactome.org; manual curation was performed to add beta-induced-gene-human-clone-3 as a target gene for Smad transcription factors. Biosimulations were run with COPASI. Transcriptomes were accessed from NCBI GEO archives, where peripheral blood mononuclear cells (PBMC) were collected from women with suspect initial mammograms, prior to undergoing diagnostic biopsy to differentiate benign from malignant cases and from women with normal initial mammograms as negative controls. Ten individual biosimulations were run in each category. Candidate biomarkers were identified by differences in simulated proteomic levels, reaction fluxes, and sensitivities analyses of simulated reactions. A 10 fold cross over design for training and testing data sets was used to assess biomarker candidates, a TGFbeta1-receptor complex, Smads, and slope of the target gene expression flux. Our results show sensitivities analyses showed differences between control and benign versus malignant biosimulation reactions. Ten reactant-reaction sensitivities were greater in malignant versus both control and benign categories. The TGFβ1:type-II-receptor:Phospho-type-I-receptor:SARA-complex was selected for analysis as a biomarker; also the reaction for BIGH3 expression was identified and the slope of the rising phase of BIGH3 expression was assessed; as were Smad3 and Smad4 levels. For predicting malignancy versus control, accuracy was 0.60, 0.85, 0.80, and 0.90; sensitivity was 0.30, 0.90, 0.70, and 0.80; specificity was 0.90, 0.80, 0.90, and 1.00; PPV was 75%, 82%, 88%, and 100%; NPV was 56%, 89%, 75%, and 83%; AUC was 0.60, 0.77, 0.72, and 0.82, respectively. These biomarkers were effective for predicting benign vs. control, but worse for distinguishing malignant from benign classifications. We conclude this method is validated for identifying candidate biomarkers for cancer. This retrospective study satisfies the requirement for this phase of discovery and evaluation of cancer biomarkers. Prospective studies are needed for successful commercialization.

Citation Format: Richard LeBaron, Greg Villareal, Dawnlee J. Roberson, Clyde Phelix. Breast cancer biomarkers from biosimulations: transcriptome-to-reactome™ technology. [abstract]. In: Proceedings of the 104th Annual Meeting of the American Association for Cancer Research; 2013 Apr 6-10; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2013;73(8 Suppl):Abstract nr 5224. doi:10.1158/1538-7445.AM2013-5224