Protein-structure-guided discovery of functional mutations across 19 cancer types

Nat Genet. 2016 Aug;48(8):827-37. doi: 10.1038/ng.3586. Epub 2016 Jun 13.


Local concentrations of mutations are well known in human cancers. However, their three-dimensional spatial relationships in the encoded protein have yet to be systematically explored. We developed a computational tool, HotSpot3D, to identify such spatial hotspots (clusters) and to interpret the potential function of variants within them. We applied HotSpot3D to >4,400 TCGA tumors across 19 cancer types, discovering >6,000 intra- and intermolecular clusters, some of which showed tumor and/or tissue specificity. In addition, we identified 369 rare mutations in genes including TP53, PTEN, VHL, EGFR, and FBXW7 and 99 medium-recurrence mutations in genes such as RUNX1, MTOR, CA3, PI3, and PTPN11, all mapping within clusters having potential functional implications. As a proof of concept, we validated our predictions in EGFR using high-throughput phosphorylation data and cell-line-based experimental evaluation. Finally, mutation-drug cluster and network analysis predicted over 800 promising candidates for druggable mutations, raising new possibilities for designing personalized treatments for patients carrying specific mutations.

Publication types

  • Research Support, N.I.H., Extramural
  • Research Support, Non-U.S. Gov't
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Algorithms
  • Antineoplastic Agents / pharmacology
  • Computational Biology / methods*
  • Databases, Pharmaceutical
  • Databases, Protein
  • Gene Expression Regulation, Neoplastic / drug effects*
  • Humans
  • Models, Molecular
  • Mutation / genetics*
  • Neoplasm Proteins / chemistry*
  • Neoplasm Proteins / genetics*
  • Neoplasm Proteins / metabolism
  • Neoplasms / drug therapy
  • Neoplasms / genetics*
  • Neoplasms / metabolism*
  • Protein Binding
  • Protein Interaction Maps
  • Protein Structure, Tertiary


  • Antineoplastic Agents
  • Neoplasm Proteins