HiLDA: a statistical approach to investigate differences in mutational signatures

Yang, Zhi; Pandey, Priyatama; Shibata, Darryl; Conti, David V.; Marjoram, Paul; Siegmund, Kimberly D.

doi:10.7717/peerj.7557

Cited by 8 publications

(9 citation statements)

References 34 publications

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“…After classifying our mutations into time of occurrence, trunk or branch, we found the signature for aging (red) was more frequent in trunk than branch mutations from MSS tumors but the same was not true for MSI tumors. This replicates the results from an earlier study of MSS colon cancers that also found a higher mutational exposure of the aging signature in trunk compared to branch mutations [14]. The lack of any new mutational signature in branch mutations, despite the different micro-environments of cancer from normal colon, is interesting.…”

Section: Discussionsupporting

confidence: 89%

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Mutational signatures in colon cancer

et al. 2019

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ObjectiveRecently, many tumor sequencing studies have inferred and reported on mutational signatures, short nucleotide patterns at which particular somatic base substitutions appear more often. A number of signatures reflect biological processes in the patient and factors associated with cancer risk. Our goal is to infer mutational signatures appearing in colon cancer, a cancer for which environmental risk factors vary by cancer subtype, and compare the signatures to those in adult stem cells from normal colon. We also compare the mutational signatures to others in the literature.ResultsWe apply a probabilistic mutation signature model to somatic mutations previously reported for six adult normal colon stem cells and 431 colon adenocarcinomas. We infer six mutational signatures in colon cancer, four being specific to tumors with hypermutation. Just two signatures explained the majority of mutations in the small number of normal aging colon samples. All six signatures are independently identified in a series of 295 Chinese colorectal cancers.

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Section: Discussionsupporting

confidence: 89%

“…Finally, we applied a hierarchical latent Dirichlet allocation model (HiLDA) [14] to test the equivalence of mutational signature exposures between trunk and branch mutations. We used the posterior distributions of the mean differences to test for differential exposures for any single signature (signature-level tests).…”

Section: Main Textmentioning

confidence: 99%

Mutational signatures in colon cancer

et al. 2019

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“…Such comparison tests are usually performed directly on the number of mutation counts or the estimated contributions. While such statistical methods presented a way to aid the process of discovering the associated mutagenic processes, a recent study by Yang et al argued that such comparison tests may struggle with low power and efficiency due to the tests ignoring the variance of the estimated contributions 14 . Especially, when the total number of observed mutations is low, which may result from low sequencing coverage, low tumor purity, and/or low mutational burden, the accuracy of the estimated contributions may vary across samples and across signatures.…”

Section: Introductionmentioning

confidence: 99%

“…Numerous methods that allow for finding de novo signatures have been introduced. These methods detect mutational signatures based on various algorithms including Bayesian NMF 4 5 , other variations of NMF 6 7 9 10 11 , expectation-maximization 12 , or mixed-membership models 13 14 . These methods can detect novel signatures including ones that resemble the composition of mutational contexts in COSMIC signatures.…”

Section: Introductionmentioning

confidence: 99%

Diffsig: Associating Risk Factors With Mutational Signatures

Park

Smith

Alsten

et al. 2023

Preprint

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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.

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“…Examples of important mutational processes with distinct mutational signatures include aging and ultraviolet (UV) radiation. Additionally, many research groups are performing analysis to discover de novo mutational signatures in cancer [1][2][3][4] .…”

Section: Introductionmentioning

confidence: 99%

iMutSig: a web application to identify the most similar mutational signature using shiny

et al. 2020

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There are two frameworks for characterizing mutational signatures which are commonly used to describe the nucleotide patterns that arise from mutational processes. Estimated mutational signatures from fitting these two methods in human cancer can be found online, in the Catalogue Of Somatic Mutations In Cancer (COSMIC) website or a GitHub repository. The two frameworks make differing assumptions regarding independence of base pairs and for that reason may produce different results. Consequently, there is a need to compare and contrast the results of the two methods, but no such tool currently exists. In this paper, we provide a simple and intuitive interface that allows comparisons of pairs of mutational signatures to be easily performed. Cosine similarity measures the extent of signature similarity. To compare mutational signatures of different formats, one signature type (COSMIC or pmsignature) is converted to the format of the other before the signatures are compared. iMutSig provides a simple and user-friendly web application allowing researchers to download published mutational signatures of either type and to compare signatures from COSMIC to those from pmsignature, and vice versa. Furthermore, iMutSig allows users to input a self-defined mutational signature and examine its similarity to published signatures from both data sources. iMutSig is accessible online and source code is available for download from GitHub.

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HiLDA: a statistical approach to investigate differences in mutational signatures

Cited by 8 publications

References 34 publications

Mutational signatures in colon cancer

Mutational signatures in colon cancer

Diffsig: Associating Risk Factors With Mutational Signatures

iMutSig: a web application to identify the most similar mutational signature using shiny

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