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A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.

ABSTRACT: 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. 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.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.SpacePAC adds a valuable tool to the identification of mutational clusters while considering protein tertiary structure.

SUBMITTER: Ryslik GA

PROVIDER: S-EPMC4227039 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature

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A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.

Ryslik Gregory A GA Cheng Yuwei Y Cheung Kei-Hoi KH Bjornson Robert D RD Zelterman Daniel D Modis Yorgo Y Zhao Hongyu H

BMC bioinformatics 20140703

<h4>Background</h4>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. Based on the hypothesis that driver mutations tend to cluster in key regions of the protein, the development ...[more]

PMID: 24990767

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Project description:BackgroundThe usefulness of circulating tumor DNA (ctDNA) in detecting mutations and monitoring treatment response has not been well studied beyond a few actionable biomarkers in non-small cell lung cancer (NSCLC).Research questionHow does the usefulness of ctDNA analysis compare with that of solid tumor biopsy analysis in patients with NSCLC?MethodsWe retrospectively evaluated 370 adult patients with NSCLC treated at the City of Hope between November 2015 and August 2019 to assess the usefulness of ctDNA in mutation identification, survival, concordance with matched tissue samples in 32 genes, and tumor evolution.ResultsA total of 1,688 somatic mutations were detected in 473 ctDNA samples from 370 patients with NSCLC. Of the 473 samples, 177 showed at least one actionable mutation with currently available Food and Drug Administration-approved NSCLC therapies. MET and CDK6 amplifications co-occurred with BRAF amplifications (false discovery rate [FDR], < 0.01), and gene-level mutations were mutually exclusive in KRAS and EGFR (FDR, 0.0009). Low cumulative percent ctDNA levels were associated with longer progression-free survival (hazard ratio [HR], 0.56; 95% CI, 0.37-0.85; P = .006). Overall survival was shorter in patients harboring BRAF mutations (HR, 2.35; 95% CI, 1.24-4.6; P = .009), PIK3CA mutations (HR, 2.77; 95% CI, 1.56-4.9; P < .001) and KRAS mutations (HR, 2.32; 95% CI, 1.30-4.1; P = .004). Gene-level concordance was 93.8%, whereas the positive concordance rate was 41.6%. More mutations in targetable genes were found in ctDNA than in tissue biopsy samples. Treatment response and tumor evolution over time were detected in repeated ctDNA samples.InterpretationAlthough ctDNA analysis exhibited similar usefulness to tissue biopsy analysis, more mutations in targetable genes were missed in tissue biopsy analyses. Therefore, the evaluation of ctDNA in conjunction with tissue biopsy samples may help to detect additional targetable mutations to improve clinical outcomes in advanced NSCLC.

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A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.

Publications

A spatial simulation approach to account for protein structure when identifying non-random somatic mutations.

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