Abstract
Background A huge volume of somatic mutations have been generated through large cancer genome sequencing projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). However, understanding the functional consequences of somatic mutations in cancer and translating the results into clinical use remains a major challenge in cancer genomic studies. Thanks to the rapid development of structural genomic technologies, such as X-ray and NMR, large amounts of protein structure data have been generated during the past decade, which enables us to map somatic mutations to protein functional features (i.e., protein-ligand binding sites) and investigate their potential impacts[1,2].
Highlights
A huge volume of somatic mutations have been generated through large cancer genome sequencing projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC)
Materials and methods In this study, we developed SGDriver, a structural genomics-based approach that incorporates protein-ligand binding sites information into the somatic missense mutation data to help understand the pathophysiological role of variations and prioritize putative druggable mutations using a Bayes inference statistical framework
We found 251 genes enriched with ligand binding site mutations in their protein products with false discovery rate less than 0.05, including 43 Cancer Gene Census (CGC) genes
Summary
A huge volume of somatic mutations have been generated through large cancer genome sequencing projects such as The Cancer Genome Atlas (TCGA) and the International Cancer Genome Consortium (ICGC). Materials and methods In this study, we developed SGDriver, a structural genomics-based approach that incorporates protein-ligand binding sites information into the somatic missense mutation data to help understand the pathophysiological role of variations and prioritize putative druggable mutations using a Bayes inference statistical framework.
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