Mathematical modeling and computation tools for tailored medicine in cancer: Analysing chimeric RNA transcripts, metabolite profiles, and protein-protein interactions of fusion proteins

Sun, 29/12/2013 - 10:30
Speaker: 
Abstract: 

Early identification of cancer is key to preventing metastasis and improving patient survival. The more sensitive a diagnostic tool, and the more information it provides on the potential susceptibility of disease cells to specific therapies, the better the chances of delivering a successful treatment regimen to individual patients. The phenomenon of chimeric RNA transcripts (i.e., fusion of two separate transcripts) in both normal and disease tissues has been well established, however, in only a few exceptions has abnormal function been associated. To identify fusion transcripts that contribute to pathogenesis or that can aid in diagnosis, I have built mathematical tools for analyzing the enormous amount of data deriving from new RNA sequencing technologies that have been applied to cancer analysis. I have found that transcript fusion events are common events in cancer, and may be useful in diagnosing cancer and selecting the most effective therapeutic strategy for individual patients. Chimeric transcripts often give rise to the expression of fusion proteins, which now can interact with a novel combination of protein partners, often combining many of the partner proteins of the two parent polypetides, as well as making new interactions with as yet unidentified partners. I have developed a systematic method based on computer algorithms and mathematical modeling for identifying significant changes to the Protein Protein Interaction (PPI) Network that occurs upon the appearance of a novel fusion protein. My goal is to map the PPI networks of cancer-associated fusion proteins and their association with cancer-related metabolic profiles using the graph theory and stochastic models, in order to uncover novel onco-genes, signaling pathways, and up- stream or downstream kinases that could be inhibited as a part of a personalized anti-cancer therapeutic regimen.