Methods for Identifying Combinations of Driver Mutations in Cancer
A major challenge in analyzing the mutations in cancer is distinguishing the handful of driver mutations that cause cancer from the majority 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 identifying driver mutations is a difficult computational problem. Many driver mutations are rare, even in large tumor cohorts, because different combinations of mutations cause cancer in different patients, such that each tumor is a "snowflake". Part of the explanation for this observation is that driver mutations target key genetic pathways that perform vital cellular functions, and each pathway can be perturbed in numerous ways.
To begin to address this challenge, we have developed computational methods to search for combinations of driver mutations targeting pathways. We first present an algorithm to identify significant clusters of mutations in an interaction network. We then present two methods for identifying combinations of mutations that are mutually exclusive in a cohort of tumors, a pattern commonly observed in known pathways. We show that these algorithms outperform previous methods on simulated and real mutation data from TCGA, and identify potentially novel combinations of mutations. We also describe the Mutation Annotation and Genome Interpretation (MAGI) web application, which displays interactive visualizations as well as crowd-sourced and text-mined mutation annotations in order to help prioritize likely driver mutations. These methods contribute towards overcoming the computational challenge of identifying driver mutations in cancer.
Bio: Max Leiserson is a postdoc at Microsoft Research New England. He received his Ph.D. from Brown University in Computer Science and Computational Biology in 2016, where he was advised by Benjamin Raphael. His research interests are in developing and applying computational methods -- including graph algorithms, combinatorial optimization, and statistics -- for improving the understanding of the genetics of disease. Recently, his research has focused on analyzing the DNA sequences of hundreds of tumors to identify the small number of key mutations responsible for cancer. Max will be joining the Computer Science Department at the University of Maryland (College Park) as an assistant professor in August 2017.