Comprehensive assessment of cancer missense mutation clustering in protein structures
Name
E5486.full.pdf
Size
1.47 MB
Format
Adobe PDF
Checksum (MD5)
add20567933adf915eb0119531e8bd2c
Author(s)
Kamburov, Atanas
Polak, Paz
Lage, Kasper
Lawrence, Michael
Leshchiner, Ignaty
Golub, Todd
Lander, Eric Steven
Getz, Gad Asher
Date Issued
September 2015
Journal
Proceedings of the National Academy of Sciences
Publisher
National Academy of Sciences (U.S.)
Citation
Kamburov, Atanas et al. “Comprehensive Assessment of Cancer Missense Mutation Clustering in Protein Structures.” Proceedings of the National Academy of Sciences 112, 40 (September 2015): E5486–E5495
Version
Final published version
Abstract
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-Mg 2+ , 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. Keywords: cancer; cancer genetics; mutation clustering; protein structures; interaction interfaces
MIT Department
Broad Institute of MIT and Harvard
Massachusetts Institute of Technology. Department of Biology
Massachusetts Institute of Technology. Department of Chemical Engineering
Terms of Use
Article is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.
Persistent DSpace Link
DOI of Published Version
https://doi.org/10.1073/PNAS.1516373112
