Abstract
BackgroundCurrent research suggests that a small set of “driver” mutations are responsible for tumorigenesis while a larger body of “passenger” mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety 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.ResultsWe 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.ConclusionSpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure.
Highlights
Current research suggests that a small set of “driver” mutations are responsible for tumorigenesis while a larger body of “passenger” mutations occur in the tumor but do not progress the disease
For a full list of which structures were found significant under SpacePAC, GraphPAC and Non-Random Mutational Clustering (NMC), see “Additional file 3: Results summary”
In this article we provide a novel algorithm to account for protein tertiary structure when identifying mutational clusters in proteins
Summary
Current research suggests that a small set of “driver” mutations are responsible for tumorigenesis while a larger body of “passenger” mutations occur in the tumor but do not progress the disease. Due to recent pharmacological successes in treating cancers caused by driver mutations, a variety of methodologies that attempt to identify such mutations have been developed. One approach is based on the idea that compared to the background mutation rate, driver mutations will have a higher frequency of non-synonymous mutations [7,8]. Several improvements to this approach have been made such as normalizing for gene length [9] as well as accounting for different mutation rates due to features such as transitions versus transversions, location of CpG sites and tumor type [10]. [5,8,12]
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