Comprehensive assessment of cancer missense mutation clustering in protein structures

• Published in: Proceedings of the National Academy of Sciences - PNAS Vol. 112; no. 40; pp. E5486 - E5495
• Main Authors: Kamburov, Atanas, Lawrence, Michael S., Polak, Paz, Leshchiner, Ignaty, Lage, Kasper, Golub, Todd R., Lander, Eric S., Getz, Gad
• Format: Journal Article
• Language: English
• Published: United States National Academy of Sciences 06.10.2015; National Acad Sciences
• Series: PNAS Plus
• Subjects: Algorithms; Biological Sciences; Cancer; Catalytic Domain; Cell Cycle Proteins - chemistry; Cell Cycle Proteins - genetics; Cell Cycle Proteins - metabolism; Cluster Analysis; Databases, Protein; Genetic Predisposition to Disease - genetics; Humans; Models, Molecular; Molecular structure; Mutation; Mutation, Missense; Neoplasms - genetics; Neoplasms - metabolism; Oncogene Proteins - chemistry; Oncogene Proteins - genetics; Oncogene Proteins - metabolism; PNAS Plus; Protein Binding; Protein Interaction Mapping - methods; Protein Interaction Maps - genetics; Protein Structure, Tertiary; Proteins; Tumors; mutation clustering; protein structures; cancer; cancer genetics; interaction interfaces
• ISSN: 0027-8424; 1091-6490
• DOI: 10.1073/pnas.1516373112

Large-scale tumor sequencing projects enabled the identification of many new cancer gene candidates through computational approaches. Here, we describe a general method to detect cancer genes based on significant 3D clustering of mutations relative to the structure of the encoded protein products. The approach can also be used to search for proteins with an enrichment of mutations at binding interfaces with a protein, nucleic acid, or small molecule partner. We applied this approach to systematically analyze the PanCancer compendium of somatic mutations from 4,742 tumors relative to all known 3D structures of human proteins in the Protein Data Bank. We detected significant 3D clustering of missense mutations in several previously known oncoproteins including HRAS, EGFR, and PIK3CA. Although clustering of missense mutations is often regarded as a hallmark of oncoproteins, we observed that a number of tumor suppressors, including FBXW7, VHL, and STK11, also showed such clustering. Beside these known cases, we also identified significant 3D clustering of missense mutations in NUF2, which encodes a component of the kinetochore, that could affect chromosome segregation and lead to aneuploidy. Analysis of interaction interfaces revealed enrichment of mutations in the interfaces between FBXW7-CCNE1, HRAS-RASA1, CUL4B-CAND1, OGT-HCFC1, PPP2R1A-PPP2R5C/PPP2R2A, DICER1-Mg²⁺, MAX-DNA, SRSF2-RNA, and others. Together, our results indicate that systematic consideration of 3D structure can assist in the identification of cancer genes and in the understanding of the functional role of their mutations.