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Cancers
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Computational Studies of Mutagenic Processes in Cancer
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Computational Studies of Mutagenic Processes in Cancer

A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: 15 December 2024 | Viewed by 7546

Special Issue Editors

Dr. Teresa M. Przytycka

E-Mail Website
Guest Editor
National Center for Biotechnology Information (NCBI), National Library of Medicine (NIH), Bethesda, MD 20894, USA
Interests: computational biology; cancer; systems biology; gene regulation; disease
Dr. Damian Wójtowicz

E-Mail Website
Guest Editor
Faculty of Mathematics, Informatics, and Mechanics, University of Warsaw, Warsaw, Poland
Interests: molecular evolution; comparative genomics; phylogenetics; Markov chains; Bayesian networks; Monte Carlo methods; algorithms for random generation; approximate counting

Special Issue Information

Dear Colleagues,

Recent advances in the computational analysis of somatic mutations in cancer genomes provided a wealth of information on mutagenic processes in cancer. For example, there is a growing recognition that mutagenic processes can be studied through the lenses of mutational signatures: characteristic mutation patterns attributed to individual mutagens. Several computational approaches were recently developed for the identification of these patterns and to link them to specific causes. However, the etiology of many mutational patterns remains unknown or not fully understood.

It was also recognized that the mutation landscape of a cancer genome is the result of a complex interaction between DNA damage, DNA repair, and other processes in the cell such as replication stress. New computational methods and analyses began to uncover and model these dependencies.

This Special Issue will focus on recent advances in the computation analysis of mutational processes, including emerging computational methods and new insights obtained through computational approaches applied to large cancer datasets.

Dr. Teresa M. Przytycka
Dr. Damian Wójtowicz
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Cancers is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • somatic mutations
  • mutational signatures
  • DNA damage
  • DNA repair
  • mutational processes and chromatin/DNA structure
  • mutational processes and cancer evolution
  • mutational processes and molecular pathways
  • mutational processes and environment

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Published Papers (3 papers)

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Research

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15 pages, 2640 KiB  
Article
Leveraging Gene Redundancy to Find New Histone Drivers in Cancer
by Daria Ostroverkhova, Daniel Espiritu, Maria J. Aristizabal and Anna R. Panchenko
Cancers 2023, 15(13), 3437; https://doi.org/10.3390/cancers15133437 - 30 Jun 2023
Viewed by 1813
Abstract
Histones play a critical role in chromatin function but are susceptible to mutagenesis. In fact, numerous mutations have been observed in several cancer types, and a few of them have been associated with carcinogenesis. Histones are peculiar, as they are encoded by a [...] Read more.
Histones play a critical role in chromatin function but are susceptible to mutagenesis. In fact, numerous mutations have been observed in several cancer types, and a few of them have been associated with carcinogenesis. Histones are peculiar, as they are encoded by a large number of genes, and the majority of them are clustered in three regions of the human genome. In addition, their replication and expression are tightly regulated in a cell. Understanding the etiology of cancer mutations in histone genes is impeded by their functional and sequence redundancy, their unusual genomic organization, and the necessity to be rapidly produced during cell division. Here, we collected a large data set of histone gene mutations in cancer and used it to investigate their distribution over 96 human histone genes and 68 different cancer types. This analysis allowed us to delineate the factors influencing the probability of mutation accumulation in histone genes and to detect new histone gene drivers. Although no significant difference in observed mutation rates between different histone types was detected for the majority of cancer types, several cancers demonstrated an excess or depletion of mutations in histone genes. As a consequence, we identified seven new histone genes as potential cancer-specific drivers. Interestingly, mutations were found to be distributed unevenly in several histone genes encoding the same protein, pointing to different factors at play, which are specific to histone function and genomic organization. Our study also elucidated mutational processes operating in genomic regions harboring histone genes, highlighting POLE as a factor of potential interest. Full article
(This article belongs to the Special Issue Computational Studies of Mutagenic Processes in Cancer)
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11 pages, 538 KiB  
Article
A Biterm Topic Model for Sparse Mutation Data
by Itay Sason, Yuexi Chen, Mark D. M. Leiserson and Roded Sharan
Cancers 2023, 15(5), 1601; https://doi.org/10.3390/cancers15051601 - 4 Mar 2023
Viewed by 1721
Abstract
Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole-exome sequencing. Methods that process sparse mutation data [...] Read more.
Mutational signature analysis promises to reveal the processes that shape cancer genomes for applications in diagnosis and therapy. However, most current methods are geared toward rich mutation data that has been extracted from whole-genome or whole-exome sequencing. Methods that process sparse mutation data typically found in practice are only in the earliest stages of development. In particular, we previously developed the Mix model that clusters samples to handle data sparsity. However, the Mix model had two hyper-parameters, including the number of signatures and the number of clusters, that were very costly to learn. Therefore, we devised a new method that was several orders-of-magnitude more efficient for handling sparse data, was based on mutation co-occurrences, and imitated word co-occurrence analyses of Twitter texts. We showed that the model produced significantly improved hyper-parameter estimates that led to higher likelihoods of discovering overlooked data and had better correspondence with known signatures. Full article
(This article belongs to the Special Issue Computational Studies of Mutagenic Processes in Cancer)
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Review

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29 pages, 2558 KiB  
Review
Computational Methods Summarizing Mutational Patterns in Cancer: Promise and Limitations for Clinical Applications
by Andrew Patterson, Abdurrahman Elbasir, Bin Tian and Noam Auslander
Cancers 2023, 15(7), 1958; https://doi.org/10.3390/cancers15071958 - 24 Mar 2023
Cited by 1 | Viewed by 3173
Abstract
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, [...] Read more.
Since the rise of next-generation sequencing technologies, the catalogue of mutations in cancer has been continuously expanding. To address the complexity of the cancer-genomic landscape and extract meaningful insights, numerous computational approaches have been developed over the last two decades. In this review, we survey the current leading computational methods to derive intricate mutational patterns in the context of clinical relevance. We begin with mutation signatures, explaining first how mutation signatures were developed and then examining the utility of studies using mutation signatures to correlate environmental effects on the cancer genome. Next, we examine current clinical research that employs mutation signatures and discuss the potential use cases and challenges of mutation signatures in clinical decision-making. We then examine computational studies developing tools to investigate complex patterns of mutations beyond the context of mutational signatures. We survey methods to identify cancer-driver genes, from single-driver studies to pathway and network analyses. In addition, we review methods inferring complex combinations of mutations for clinical tasks and using mutations integrated with multi-omics data to better predict cancer phenotypes. We examine the use of these tools for either discovery or prediction, including prediction of tumor origin, treatment outcomes, prognosis, and cancer typing. We further discuss the main limitations preventing widespread clinical integration of computational tools for the diagnosis and treatment of cancer. We end by proposing solutions to address these challenges using recent advances in machine learning. Full article
(This article belongs to the Special Issue Computational Studies of Mutagenic Processes in Cancer)
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