Next-generation DNA sequencing technologies now enable the measurement of somatic mutations across many cancer samples. A key challenge in interpreting data from these studies is to distinguish the functional (driver) mutations responsible for cancer from random (passenger) mutations. A common approach to this problem is to find genomic regions -- e.g. single positions or genes -- that are recurrently mutated in a significant number of samples. This approach is complicated by the fact that cancer is genetically heterogeneous with different samples exhibiting different combinations of driver mutations. I will describe two algorithms to identify driver pathways, groups of genes containing driver mutations, in a large cohort of cancer samples. I will illustrate the applications of these algorithms to mutation data from The Cancer Genome Atlas.
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