Utilizing protein structure to identify non-random somatic mutations

Abstract

BackgroundHuman cancer is caused by the accumulation of somatic mutations in tumor suppressors and oncogenes within the genome. In the case of oncogenes, recent theory suggests that there are only a few key “driver” mutations responsible for tumorigenesis. As there have been significant pharmacological successes in developing drugs that treat cancers that carry these driver mutations, several methods that rely on mutational clustering have been developed to identify them. However, these methods consider proteins as a single strand without taking their spatial structures into account. We propose an extension to current methodology that incorporates protein tertiary structure in order to increase our power when identifying mutation clustering.ResultsWe have developed iPAC (identification of Protein Amino acid Clustering), an algorithm that identifies non-random somatic mutations in proteins while taking into account the three dimensional protein structure. By using the tertiary information, we are able to detect both novel clusters in proteins that are known to exhibit mutation clustering as well as identify clusters in proteins without evidence of clustering based on existing methods. For example, by combining the data in the Protein Data Bank (PDB) and the Catalogue of Somatic Mutations in Cancer, our algorithm identifies new mutational clusters in well known cancer proteins such as KRAS and PI3KC α. Further, by utilizing the tertiary structure, our algorithm also identifies clusters in EGFR, EIF2AK2, and other proteins that are not identified by current methodology. The R package is available at: http://www.bioconductor.org/packages/2.12/bioc/html/iPAC.html.ConclusionOur algorithm extends the current methodology to identify oncogenic activating driver mutations by utilizing tertiary protein structure when identifying nonrandom somatic residue mutation clusters.

Functional consequences of somatic mutations in cancer using protein pocket-based prioritization approach

“…With an increasing amount of both protein structural data in the PDB database and somatic mutation data generated by next-generation sequencing (NGS) experiments, the integration of protein structural information and largescale somatic mutations offers an alternative, promising approach to uncovering functionally important somatic mutations in cancer. Several recent studies have demonstrated that disease-causing mutations commonly alter protein folding, protein stability, and proteinprotein interactions (PPIs), often leading to new disease phenotypes [8][9][10][11][12][13][14][15][16][17][18][19][20]. Espinosa et al [21] proposed a predictor, InCa (Index of Carcinogenicity) that integrates somatic mutation profiles from the Catalogue of Somatic Mutations in Cancer (COSMIC) database and the neutral mutations from the 1000 Genomes project into protein structure and interaction interface information.…”

Expanding the computational toolbox for mining cancer genomes

“…This heuristic is applicable to many well-known cancer genes, but is also somewhat arbitrary in the use of a fixed 20% threshold. It is now being supplemented by algorithms that assess patterns of mutational signatures 100 and clustering of mutations in protein sequence 101 or 3D protein structure 102 using more rigorous statistical scores. Recent methods have shown that combining different signals of positive selection holds great potential for finding reliable lists of driver genes 103 .…”

Exome-Scale Discovery of Hotspot Mutation Regions in Human Cancer Using 3D Protein Structure

“…A recent study suggested that ~1500 cases of endometrial cancer would need to be sequenced in order to attain 90% power to detect mutations in 90% of genes with a mutation frequency of 2% with the SMG approach (5). The recognition of the limitations of the SMG paradigm has motivated interest in orthogonal analysis techniques to detect mutational patterns associated with drivers (1,6–9). …”

“…Gene- and protein domain-level testing may indicate the possibility of a 3D hotspot but cannot identify the specific positions in the hotspot. An algorithm that leverages 3D protein structure information, but still performs clustering in 1D through a dimensionality reduction step, has shown utility in detecting OGs (9). A recent study of an aggregated collection of TCGA cancer mutations from 21 tumor types presented an algorithm to identify cancer genes based on 3D clustering of somatic missense mutations, yielding ten such genes.…”