Dataset Information

Diffsig: Associating Risk Factors With Mutational Signatures.

ABSTRACT: Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present Diffsig, a model and R package for estimating the association of risk factors with mutational signatures, suggesting etiologies for the pre-defined mutational signatures. Diffsig is a Bayesian Dirichlet-multinomial hierarchical model that allows testing of any type of risk factor while taking into account the uncertainty associated with samples with a low number of observations. In simulation, we found that our method can accurately estimate risk factor-mutational signal associations. We applied Diffsig to breast cancer data to assess relationships between five established breast-relevant mutational signatures and etiologic variables, confirming known mechanisms of cancer development. Diffsig is implemented as an R package available at: https://github.com/jennprk/diffsig.

SUBMITTER: Park JE

PROVIDER: S-EPMC9934616 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature

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Diffsig: Associating Risk Factors With Mutational Signatures.

Park Ji-Eun JE Smith Markia A MA Van Alsten Sarah C SC Walens Andrea A Wu Di D Hoadley Katherine A KA Troester Melissa A MA Love Michael I MI

bioRxiv : the preprint server for biology 20230210

Somatic mutational signatures elucidate molecular vulnerabilities to therapy and therefore detecting signatures and classifying tumors with respect to signatures has clinical value. However, identifying the etiology of the mutational signatures remains a statistical challenge, with both small sample sizes and high variability in classification algorithms posing barriers. As a result, few signatures have been strongly linked to particular risk factors. Here we present <i>Diffsig</i>, a model and ...[more]

PMID: 36798154

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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.

Dataset Information

Diffsig: Associating Risk Factors With Mutational Signatures.

Publications

Diffsig: Associating Risk Factors With Mutational Signatures.

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