title: Structure-Based Analysis Reveals Cancer Missense Mutations Target Protein Interaction Interfaces.
abstract: Recently it has been shown that cancer mutations selectively target protein-protein interactions. We hypothesized that mutations affecting distinct protein interactions involving established cancer genes could contribute to tumor heterogeneity, and that novel mechanistic insights might be gained into tumorigenesis by investigating protein interactions under positive selection in cancer. To identify protein interactions under positive selection in cancer, we mapped over 1.2 million nonsynonymous somatic cancer mutations onto 4,896 experimentally determined protein structures and analyzed their spatial distribution. In total, 20% of mutations on the surface of known cancer genes perturbed protein-protein interactions (PPIs), and this enrichment for PPI interfaces was observed for both tumor suppressors (Odds Ratio 1.28, P-value < 10(-4)) and oncogenes (Odds Ratio 1.17, P-value < 10(-3)). To study this further, we constructed a bipartite network representing structurally resolved PPIs from all available human complexes in the Protein Data Bank (2,864 proteins, 3,072 PPIs). Analysis of frequently mutated cancer genes within this network revealed that tumor-suppressors, but not oncogenes, are significantly enriched with functional mutations in homo-oligomerization regions (Odds Ratio 3.68, P-Value < 10(-8)). We present two important examples, TP53 and beta-2-microglobulin, for which the patterns of somatic mutations at interfaces provide insights into specifically perturbed biological circuits. In patients with TP53 mutations, patient survival correlated with the specific interactions that were perturbed. Moreover, we investigated mutations at the interface of protein-nucleotide interactions and observed an unexpected number of missense mutations but not silent mutations occurring within DNA and RNA binding sites. Finally, we provide a resource of 3,072 PPI interfaces ranked according to their mutation rates. Analysis of this list highlights 282 novel candidate cancer genes that encode proteins participating in interactions that are perturbed recurrently across tumors. In summary, mutation of specific protein interactions is an important contributor to tumor heterogeneity and may have important implications for clinical outcomes.
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, United States of America.
authors.forename: H Billur
authors.initials: HB
authors.lastname: Engin
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, United States of America.
authors.forename: Jason F
authors.initials: JF
authors.lastname: Kreisberg
authors.affiliation: Division of Medical Genetics, Department of Medicine, University of California, San Diego, 9500 Gilman Dr., La Jolla, CA, 92093, United States of America.
authors.forename: Hannah
authors.initials: H
authors.lastname: Carter
mesh_terms: D001665:Binding Sites; D019295:Computational Biology / Q000379:methods; D030541:Databases, Genetic; D006801:Humans; D008958:Models, Molecular; D050505:Mutant Proteins / Q000737:chemistry* / Q000378:metabolism*; D020125:Mutation, Missense; D009369:Neoplasms / Q000235:genetics* / Q000378:metabolism*; D011485:Protein Binding; D011487:Protein Conformation; D025941:Protein Interaction Mapping / Q000379:methods; D060066:Protein Interaction Maps; D055503:Protein Multimerization; D013329:Structure-Activity Relationship; D016159:Tumor Suppressor Protein p53 / Q000235:genetics / Q000378:metabolism
chemical_list: D050505:Mutant Proteins; D016159:Tumor Suppressor Protein p53