  Background: Current research suggests that a small set of "driver" mutations
are responsible for tumorigenesis while a larger body of "passenger" mutations
occurs in the tumor but does not progress the disease. Due to recent
pharmacological successes in treating cancers caused by driver mutations, a
variety of of methodologies that attempt to identify such mutations have been
developed. Based on the hypothesis that driver mutations tend to cluster in key
regions of the protein, the development of cluster identification algorithms
has become critical.
  Results: We have developed a novel methodology, SpacePAC (Spatial Protein
Amino acid Clustering), that identifies mutational clustering by considering
the protein tertiary structure directly in 3D space. By combining the
mutational data in the Catalogue of Somatic Mutations in Cancer (COSMIC) and
the spatial information in the Protein Data Bank (PDB), SpacePAC is able to
identify novel mutation clusters in many proteins such as FGFR3 and CHRM2. In
addition, SpacePAC is better able to localize the most significant mutational
hotspots as demonstrated in the cases of BRAF and ALK. The R package is
available on Bioconductor at:
http://www.bioconductor.org/packages/release/bioc/html/SpacePAC.html
  Conclusion: SpacePAC adds a valuable tool to the identification of mutational
clusters while considering protein tertiary structure
