Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery: Bridging Experiments and Computations - September 10-14, 2014, Istanbul, Turkey

Modeling of Biomolecular Systems Interactions, Dynamics, and Allostery Poster Session II

86-POS Board 39 Revealing Clinically Relevant Targets across Various Glioblastoma Tumor Lines by Integrating Multiple Omic Data Nurcan Tuncbag 1,2 , Jenny Pokorny 3 , Pamela Milani 1 , Jann Sarkaria 3 , Ernest Fraenkel 1 . 1 Massachusetts Institute of Technology, Cambridge, MA, USA, 3 Mayo Clinic College of Medicine, Rochester, MN, USA, 4 Massachusetts Institute of Technology, Cambridge, MA, USA. 2 Middle East Technical University, Ankara, Turkey, With the help of high-throughput technologies, we are able to comprehensively monitor quantitative molecular changes within signaling networks at many levels in response to perturbations or disease. Although a single omic dataset provides a wealth of information about a given biological problem, it is limited in the ability to fully capture a cellular pathway that is altered in a given experimental setting. Therefore, integrating multiple data sources together in a network context is crucial to find the omissions in each set, explore hidden entities and identify therapeutic targets. In this work, we reconstruct signaling networks across eight implemented xenograft models of Glioblastoma multiforme (GBM, the most aggressive type of malignant brain tumors) patients by integrating phosphoproteomic and interactome data. The prize- collecting Steiner forest (PCSF) algorithm has been used to reconstruct networks by taking into account confidence that the phosphoproteomic hits are significant and the probability that each reported protein-protein interaction is real. Simultaneous comparison of reconstructed networks led us to find out several common proteins and pathways across multiple tumor lines that were not identified in the phosphoproteomic dataset. For example, Nfkb signaling and mTOR pathways were common in most of the patients, as were proteins such as Mek1, Stat6, Erbb3. We have experimentally shown that Mek1 is stably over-phosphorylated in all tumor lines compared to normal human astrocytes. Another target, Numb, was found to be an invasiveness marker in this setup. Tumor lines having Numb present in their networks were in minimally invasive class while remaining were in highly invasive class. Overall, despite the heterogeneity across various tumor lines of GBM, common pathways and proteins revealed here have important outcomes in GBM and our integrative network modeling approach help in finding testable therapeutic targets.

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