A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

Sason, Itay; Wójtowicz, Damian; Robinson, Welles; Leiserson, Mark D.M.; Przytycka, Teresa M.; Sharan, Roded

doi:10.1007/978-3-030-17083-7_15

Cited by 4 publications

(5 citation statements)

References 22 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To test our novel models we work with Breast Cancer (BRCA), Malignant Lymphoma (MALY) and Chronic Lymphocytic Leukemia (CLLE) from International Cancer Genome Consortiom as in [13] (see Table 1). We executed 2-fold cross validation by dividing each sample into two equally-sized subsets.…”

Section: Data Descriptionmentioning

confidence: 99%

“…Deciphering these signatures and the genome’s exposure to them are key to understanding how it is shaped by the disease. Such mapping was initially done by non-negative matrix factorization (NMF) and its generalizations [1, 6, 7, 8, 9], or refitting methods that infer the exposures given the signa-tures [10, 11, 12, 13, 14]. More recent work built on topic models that allow to rigorously attribute likelihood to the data and solve the models’ parameters by maximizing it [15, 16, 17, 18, 19].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A mutation-level covariate model for mutational signatures

Kahane

Leiserson

Sharan

2022

Preprint

Self Cite

View full text Add to dashboard Cite

Mutational processes and their exposures in particular genomes are key to our understanding of how these genomes are shaped. However, current analyses assume that these processes are uniformly active across the genome without accounting for potential covariates such as strand or genomic region that could impact such activities. Here we suggest the first mutation-covariate models that explicitly model the effect of different covariates on the exposures of mutational processes. We apply these models to test the impact of replication strand on these processes and compare them to strand-oblivious models across a range of data sets. Our models capture replication strand specificity, point to signatures affected by it, and score better on held-out data compared to standard models that do not account for mutation-level covariate information.

show abstract

Section: Data Descriptionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A mutation-level covariate model for mutational signatures

Kahane

Leiserson

Sharan

2022

Preprint

Self Cite

View full text Add to dashboard Cite

show abstract

“…TensorSignatures ( 45 ) has been recently developed based on an overdispersed statistical model incorporating mutational catalogues, transcription and replication strand bias, and kataegis, leading to more robust extraction of mutation signatures. SigMa ( 46 ) and recently StickySig ( 47 ) model statistical dependencies among neighboring mutations to characterize strand coordination, and other genomic and nongenomic factors that influence the activity of mutation signatures. Such efforts are exciting and contributing to the broader understanding of the patterns of the mutational signatures in the genome.…”

Section: Computational Resources For Extraction and Analysis Of Mutatmentioning

confidence: 99%

Characteristics of mutational signatures of unknown etiology

2020

NAR Cancer

View full text Add to dashboard Cite

Although not all somatic mutations are cancer drivers, their mutational signatures, i.e. the patterns of genomic alterations at a genome-wide scale, provide insights into past exposure to mutagens, DNA damage and repair processes. Computational deconvolution of somatic mutation patterns and expert curation pan-cancer studies have identified a number of mutational signatures associated with point mutations, dinucleotide substitutions, insertions and deletions, and rearrangements, and have established etiologies for a subset of these signatures. However, the mechanisms underlying nearly one-third of all mutational signatures are not yet understood. The signatures with established etiology and those with hitherto unknown origin appear to have some differences in strand bias, GC content and nucleotide context diversity. It is possible that some of the hitherto ‘unknown’ signatures predominantly occur outside gene regions. While nucleotide contexts might be adequate to establish etiologies of some mutational signatures, in other cases additional features, such as broader (epi)genomic contexts, including chromatin, replication timing, processivity and local mutational patterns, may help fully understand the underlying DNA damage and repair processes. Nonetheless, remarkable progress in characterization of mutational signatures has provided fundamental insights into the biology of cancer, informed disease etiology and opened up new opportunities for cancer prevention, risk management, and therapeutic decision making.

show abstract

“…We developed an Expectation Maximization (EM) algorithm to learn the parameters of this model from data. The EM algorithm is described in (Sason et al 2019) and runs in O(T n) time per iteration for T mutations and n signatures. The EM model training is controlled by two parameters: Maximum number of iterations and Tolerance, which is used to decide on convergence when the relative improvement in log-likelihood falls below it, and is set to 1e − 10 throughout.…”

Section: Model Specification and Trainingmentioning

confidence: 99%

A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

et al. 2020

Self Cite

View full text Add to dashboard Cite

The characterization of mutational processes in terms of their signatures of activity relies mostly on the assumption that mutations in a given cancer genome are independent of one another. Recently, it was discovered that certain segments of mutations, termed processive groups, occur on the same DNA strand and are generated by a single process or signature. Here we provide a first probabilistic model of mutational signatures that accounts for their observed stickiness and strand coordination. The model conditions on the observed strand for each mutation and allows the same signature to generate a run of mutations. It can both use known signatures or learn new ones. We show that this model provides a more accurate description of the properties of mutagenic processes than independent-mutation achieving substantially higher likelihood on held-out data. We apply this model to characterize the processivity of mutagenic processes across multiple types of cancer.

show abstract

A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

Cited by 4 publications

References 22 publications

A mutation-level covariate model for mutational signatures

A mutation-level covariate model for mutational signatures

Characteristics of mutational signatures of unknown etiology

A Sticky Multinomial Mixture Model of Strand-Coordinated Mutational Processes in Cancer

Contact Info

Product

Resources

About