Comprehensive assessment of cancer missense mutation clustering in protein structures

  • Atanas Kamburov
    Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, MA 02114;
  • Michael S. Lawrence
    Broad Institute of MIT and Harvard, Cambridge, MA 02142;
  • Paz Polak
    Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, MA 02114;
  • Ignaty Leshchiner
    Broad Institute of MIT and Harvard, Cambridge, MA 02142;
  • Kasper Lage
    Harvard Medical School, Boston, MA 02115;
  • Todd R. Golub
    Broad Institute of MIT and Harvard, Cambridge, MA 02142;
  • Eric S. Lander
    Broad Institute of MIT and Harvard, Cambridge, MA 02142;
  • Gad Getz
    Department of Pathology and Cancer Center, Massachusetts General Hospital, Boston, MA 02114;

書誌事項

公開日
2015-09-21
権利情報
  • http://www.pnas.org/site/misc/userlicense.xhtml
DOI
  • 10.1073/pnas.1516373112
公開者
Proceedings of the National Academy of Sciences

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説明

<jats:title>Significance</jats:title><jats:p>Tumor sequencing efforts have enabled the identification of cancer genes based on an excess of mutations in the gene or clustering of mutations along the (one-dimensional) DNA sequence of the gene. Here, we show that this approach can be extended to identify cancer genes based on clustering of mutations relative to the 3D structure of the protein product. By analyzing the PanCancer compendium of somatic mutations in nearly 5,000 tumors, we identified known cancer genes and previously unidentified candidates based on clustering of missense mutations in protein structures or at interfaces with binding partners. In addition, we found that 3D clustering is present in both oncoproteins and tumor suppressors—contrary to the view that such clustering is a hallmark of oncoproteins.</jats:p>

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