Extracting significant sample-specific cancer mutations using their protein interactions

We present a joint analysis method for mutation and gene expression data employing information about proteins that are highly interconnected at the level of protein to protein (pp) interactions, which we apply to the TCGA Acute Myeloid Leukemia (AML) dataset. Given the low incidence of most mutations in virtually all cancer types, as well as the significant inter-patient heterogeneity of the mutation landscape, determining the true causal mutations in each individual patient remains one of the most important challenges for personalized cancer diagnostics and therapy. More automated methods are needed for determining these "driver" mutations in each individual patient. For this purpose, we are exploiting two types of contextual information: (1) the pp interactions of the mutated genes, as well as (2) their potential correlations with gene expression clusters. The use of pp interactions is based on our surprising finding that most AML mutations tend to affect nontrivial protein to protein interaction cliques.