Statistical Detection and Biochemical Classification of Cancer Driver Mutation Patterns in Biological Networks

We will develop and combine advance sequence variation analyses with complementary biological network analyses into a highly novel systems biology approach that will: i) detect sets of related mutations in driver regulatory/signaling pathways, ii) classify these pathways as stimulated, inhibited, or mixed with respect to their role in the tumor development process, and iii) predict direct metabolic outcomes of these perturbed pathways. This biochemical interpretation of aggregated tumor mutations from driver mutation gene sets to inhibited/stimulated pathways to perturbed biological network will provide new mechanistic insights in tumor progression at a systems level. Also with this information, potential drug targets in the detected driver pathways can be classified as requiring agonist or antagonist drug development, making drug target evaluation and prioritization much more effective. Furthermore, identification of co-occurrence between specific genes and pathways may aid in the development of multi-therapeutic cancer treatments that are optimized to groups of patients showing the same mutational patterns of co-occurrence.