Dataset Information

Significance of duon mutations in cancer genomes.

ABSTRACT: Functional mutations in coding regions not only affect the structure and function of the protein products, but may also modulate their expression in some cases. This class of mutations, recently dubbed "duon mutations" due to their dual roles, can potentially have major impacts on downstream pathways. However their significance in diseases such as cancer remain unclear. In a survey covering 4606 samples from 19 cancer types, and integrating allelic expression, overall mRNA expression, regulatory motif perturbation, and chromatin signatures in one composite index called REDACT score, we identified potential duon mutations. Several such mutations are detected in known cancer genes in multiple cancer types. For instance a potential duon mutation in TP53 is associated with increased expression of the mutant allelic gene copy, thereby possibly amplifying the functional effects on the downstream pathways. Another potential duon mutation in SF3B1 is associated with abnormal splicing and changes in angiogenesis and matrix degradation related pathways. Our findings emphasize the need to interrogate the mutations in coding regions beyond their obvious effects on protein structures.

SUBMITTER: Yadav VK

PROVIDER: S-EPMC4897603 | biostudies-literature | 2016 Jun

REPOSITORIES: biostudies-literature

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Significance of duon mutations in cancer genomes.

Yadav Vinod Kumar VK Smith Kyle S KS Flinders Colin C Mumenthaler Shannon M SM De Subhajyoti S

Scientific reports 20160608

Functional mutations in coding regions not only affect the structure and function of the protein products, but may also modulate their expression in some cases. This class of mutations, recently dubbed "duon mutations" due to their dual roles, can potentially have major impacts on downstream pathways. However their significance in diseases such as cancer remain unclear. In a survey covering 4606 samples from 19 cancer types, and integrating allelic expression, overall mRNA expression, regulatory ...[more]

PMID: 27272679

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Project description:Identifying cancer-causing mutations from sequenced cancer genomes hold much promise for targeted therapy and precision medicine. "Driver" mutations are primarily responsible for cancer progression, while "passengers" are functionally neutral. Although several computational approaches have been developed for distinguishing between driver and passenger mutations, very few have concentrated on using the raw nucleotide sequences surrounding a particular mutation as potential features for building predictive models. Using experimentally validated cancer mutation data in this study, we explored various string-based feature representation techniques to incorporate information on the neighborhood bases immediately 5' and 3' from each mutated position. Density estimation methods showed significant distributional differences between the neighborhood bases surrounding driver and passenger mutations. Binary classification models derived using repeated cross-validation experiments provided comparable performances across all window sizes. Integrating sequence features derived from raw nucleotide sequences with other genomic, structural, and evolutionary features resulted in the development of a pan-cancer mutation effect prediction tool, NBDriver, which was highly efficient in identifying pathogenic variants from five independent validation datasets. An ensemble predictor obtained by combining the predictions from NBDriver with three other commonly used driver prediction tools (FATHMM (cancer), CONDEL, and MutationTaster) significantly outperformed existing pan-cancer models in prioritizing a literature-curated list of driver and passenger mutations. Using the list of true positive mutation predictions derived from NBDriver, we identified a list of 138 known driver genes with functional evidence from various sources. Overall, our study underscores the efficacy of using raw nucleotide sequences as features to distinguish between driver and passenger mutations from sequenced cancer genomes.

Project description:ObjectiveEpithelial ovarian cancer (EOC) is a heterogeneous disease with diverse clinicopathological features and behaviors, and its heterogeneity may be concerned with the accumulation of multiple somatic oncogenic mutations. The major goals of this study are to systematically perform the comprehensive mutational profiling in EOC patients, and investigate the associations between somatic mutations and clinicopathological characteristics.MethodsA total of 80 surgical specimens were obtained from EOC patients who had previously undergone primary debulking surgery, and genomic DNAs were extracted from fresh-frozen tissues. We investigated mutational status in hot spot regions of 50 cancer-related genes by targeted next-generation sequencing using an Ion AmpliSeq Cancer Hotspot Panel v2 Kit.ResultsValidated mutations were detected in 66 of the 80 tumors (82.5%). The five most frequently mutated genes were TP53 (43.8%), PIK3CA (27.5%), KRAS (23.8%), PTEN (10%) and CTNNB1 (10%). PTEN and CTNNB1 mutations were associated with younger age. PIK3CA1, KRAS and CTNNB1 mutations were observed in early-stage, whereas TP53 mutations were more common in advanced stage. Significant associations were observed between TP53 mutation and serous carcinoma, and between KRAS mutation and mucinous carcinoma. Both PIK3CA mutation and CTNNB1 mutation were also significantly associated with endometrioid and clear cell carcinoma. The patients with PIK3CA and KRAS mutations were significantly associated with favorable progression free survival (PFS). In particular, PIK3CA mutations had more significant associations with favorable PFS than PIK3CA wild-type in the endometrioid subtype (P = 0.012). Patients with mutations only in TP53 were significantly associated with worse PFS.ConclusionEOCs were heterogeneous at the genomic level and harbored somatic oncogenic mutations. Our molecular profiling may have the potential for becoming a novel stratification within histological subtypes of EOC. Further studies are needed to define molecular classification for improved clinical outcomes and treatment of EOC patients in future.

Dataset Information

Significance of duon mutations in cancer genomes.

Publications

Significance of duon mutations in cancer genomes.

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