Methods for Analyzing Mutations in Cancer

Talk
Max Leiserson
University of Maryland
Talk Series: 
Time: 
11.17.2017 11:00 to 12:00
Location: 

CSI 2117

A major challenge in analyzing the mutations in cancer is distinguishing the handful of driver mutations that cause cancer from the multitude of passenger mutations that play no role in cancer. In part to address this challenge, consortia such as The Cancer Genome Atlas (TCGA) have generated massive catalogues of somatic mutations in thousands of tumors. The new wealth of mutation data has shown that there are many computational challenges that must be overcome to identify driver mutations. Many driver mutations occur at low frequencies so that they are rare, even in large tumor cohorts. Part of the reason for this phenomenon is that driver mutations target key genetic pathways that perform vital cellular functions, and each pathway can be perturbed in numerous ways. Thus, different combinations of mutations cause cancer in different patients. To begin to address this challenge, my collaborators and I have developed computational methods to search for combinations of driver mutations targeting pathways. First, I will present an algorithm to identify significant clusters of mutations in connected subgraphs of an interaction network. I will then present methods for identifying combinations of mutations that are mutually exclusive in a cohort of tumors, a pattern commonly observed in known cancer pathways. I will show that these algorithms identify known and novel driver mutations on real mutation data from TCGA, and identify potentially novel combinations of mutations. These methods contribute towards overcoming the computational challenge of identifying driver mutations in cancer. I will conclude by discussing ongoing research projects in my group that seek to identify mutation signatures in cancer genomes, and to use these signatures as biomarkers for targeted therapy.