Dataset Information

Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations.

ABSTRACT: Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of 50 known cancer driver mutations by analysing recurrence in our cohort and annotations of known cancer-associated genes from the Cancer Gene Census, IntOGen database and Cancer Genome Interpreter. We then use these datasets to perform binary univariate logistic regression and establish the statistical relationship between individual driver mutations and known mutational signatures across different cancer types. Our analysis led to the identification of 39 significant associations between driver mutations and mutational signatures (P < 0.004, with a false discovery rate of < 5%). We first validate our methodology by establishing statistical links for known and novel associations between driver mutations and the mutational signature arising from Polymerase Epsilon proofreading deficiency. We then examine associations between driver mutations and mutational signatures for AID/APOBEC enzyme activity and deficient mismatch repair. We also identify negative associations (odds ratio < 1) between mutational signatures and driver mutations, and here we examine the role of aging and cigarette smoke mutagenesis in the generation of driver mutations in IDH1 and KRAS in brain cancers and lung adenocarcinomas respectively. Our study provides statistical foundations for hypothesised links between otherwise independent biological processes and we uncover previously unexplored relationships between driver mutations and mutagenic processes during cancer development. These associations give insights into how cancers acquire advantageous mutations and can provide direction to guide further mechanistic studies into cancer pathogenesis.

SUBMITTER: Poulos RC

PROVIDER: S-EPMC6249022 | biostudies-literature | 2018 Nov

REPOSITORIES: biostudies-literature

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Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations.

Poulos Rebecca C RC Wong Yuen T YT Ryan Regina R Pang Herbert H Wong Jason W H JWH

PLoS genetics 20181109 11

Driver mutations are the genetic variants responsible for oncogenesis, but how specific somatic mutational events arise in cells remains poorly understood. Mutational signatures derive from the frequency of mutated trinucleotides in a given cancer sample, and they provide an avenue for investigating the underlying mutational processes that operate in cancer. Here we analyse somatic mutations from 7,815 cancer exomes from The Cancer Genome Atlas (TCGA) across 26 cancer types. We curate a list of ...[more]

PMID: 30412573

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Project description:PurposeExome- and whole-genome sequencing of muscle-invasive bladder cancer has revealed important insights into the molecular landscape; however, there are few studies of non-muscle-invasive bladder cancer with detailed risk factor information.Experimental designWe examined the relationship between smoking and other bladder cancer risk factors and somatic mutations and mutational signatures in bladder tumors. Targeted sequencing of frequently mutated genes in bladder cancer was conducted in 322 formalin-fixed paraffin-embedded bladder tumors from a population-based case-control study. Logistic regression was used to calculate odds ratios (OR) and 95% confidence intervals (CI), evaluating mutations and risk factors. We used SignatureEstimation to extract four known single base substitution mutational signatures and Poisson regression to calculate risk ratios (RR) and 95% CIs, evaluating signatures and risk factors.ResultsNon-silent KDM6A mutations were more common in females than males (OR = 1.83; 95% CI, 1.05-3.19). There was striking heterogeneity in the relationship between smoking status and established single base substitution signatures: current smoking status was associated with greater ERCC2-Signature mutations compared with former (P = 0.024) and never smoking (RR = 1.40; 95% CI, 1.09-1.80; P = 0.008), former smoking was associated with greater APOBEC-Signature13 mutations (P = 0.05), and never smoking was associated with greater APOBEC-Signature2 mutations (RR = 1.54; 95% CI, 1.17-2.01; P = 0.002). There was evidence that smoking duration (the component most strongly associated with bladder cancer risk) was associated with ERCC2-Signature mutations and APOBEC-Signature13 mutations among current (P trend = 0.005) and former smokers (P = 0.0004), respectively.ConclusionsThese data quantify the contribution of bladder cancer risk factors to mutational burden and suggest different signature enrichments among never, former, and current smokers.

Project description:PET-based radiomics have been used to noninvasively quantify the metabolic tumor phenotypes; however, little is known about the relationship between these phenotypes and underlying somatic mutations. This study assessed the association and predictive power of 18F-FDG PET-based radiomic features for somatic mutations in non-small cell lung cancer patients. Methods: Three hundred forty-eight non-small cell lung cancer patients underwent diagnostic 18F-FDG PET scans and were tested for genetic mutations. Thirteen percent (44/348) and 28% (96/348) of patients were found to harbor epidermal growth factor receptor (EGFR) or Kristen rat sarcoma viral (KRAS) mutations, respectively. We evaluated 21 imaging features: 19 independent radiomic features quantifying phenotypic traits and 2 conventional features (metabolic tumor volume and maximum SUV). The association between imaging features and mutation status (e.g., EGFR-positive [EGFR+] vs. EGFR-negative) was assessed using the Wilcoxon rank-sum test. The ability of each imaging feature to predict mutation status was evaluated by the area under the receiver operating curve (AUC) and its significance was compared with a random guess (AUC = 0.5) using the Noether test. All P values were corrected for multiple hypothesis testing by controlling the false-discovery rate (FDRWilcoxon, FDRNoether) with a significance threshold of 10%. Results: Eight radiomic features and both conventional features were significantly associated with EGFR mutation status (FDRWilcoxon = 0.01-0.10). One radiomic feature (normalized inverse difference moment) outperformed all other features in predicting EGFR mutation status (EGFR+ vs. EGFR-negative, AUC = 0.67, FDRNoether = 0.0032), as well as differentiating between KRAS-positive and EGFR+ (AUC = 0.65, FDRNoether = 0.05). None of the features was associated with or predictive of KRAS mutation status (KRAS-positive vs. KRAS-negative, AUC = 0.50-0.54). Conclusion: Our results indicate that EGFR mutations may drive different metabolic tumor phenotypes that are captured in PET images, whereas KRAS-mutated tumors do not. This proof-of-concept study sheds light on genotype-phenotype interactions, using radiomics to capture and describe the phenotype, and may have potential for developing noninvasive imaging biomarkers for somatic mutations.

Dataset Information

Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations.

Publications

Analysis of 7,815 cancer exomes reveals associations between mutational processes and somatic driver mutations.

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