Biology subjects

Senkin, S.

Publications and source records attributed to Senkin, S..

5 recordsLinked to original sources

bioRxiv2025

Genome-scale multi-organ analysis of mutagenic effects of ethanol and acetaldehyde in Sprague Dawley rats

Alcohol consumption is a major cancer risk factor, particularly for head and neck cancers, including the oral cavity. Acetaldehyde, the primary genotoxic metabolite of ethanol, may play key roles in oral carcinogenesis, though the mechanisms remain unclear. While mutational signatures SBS16, DBS4, and ID11 have been tentatively linked to alcohol use, they are not exclusive to alcohol-related cancers. In this study, we examined the genome-wide in vivo mutagenic effects of ethanol and acetaldehyde by analyzing tumors from the cheek, Zymbal gland, larynx, forestomach, and liver of rats chronically exposed to these compounds. Signature analysis revealed exposure-specific, early-onset formation of SBS17 in [~]28% of head and neck tumors, suggesting inflammation and/or oxidative damage as potential mediators of carcinogenesis. Cancer driver gene analysis identified a relative enrichment of exposed tumors with mutations in the Tp53 and Mtor genes. No notable exposure-specific changes were observed in doublet-base substitutions, indel signatures, or copy number variants. Notably, SBS16, DBS4, and ID11 were absent. Our findings suggest direct mutagenicity may not be the main driver of alcohol-related cancer. Other harmful cellular effects, undetectable by whole genome sequencing, may be involved. Our findings suggest that SBS17 could function as a potential exposure-specific molecular marker of alcohol-related cancers in humans.

cancer biology↗

bioRxiv2024

Human cancer genomes harbor the mutational signature of tobacco-specific nitrosamines NNN and NNK

Tobacco usage is linked to multiple cancer types and accounts for a quarter of all cancer-related deaths. Tobacco smoke contains various carcinogenic compounds, including polycyclic aromatic hydrocarbons (PAH), though the mutagenic potential of many tobacco-related chemicals remains largely unexplored. In particular, the highly carcinogenic tobacco-specific nitrosamines NNN and NNK form pre-mutagenic pyridyloxobutyl (POB) DNA adducts. In the study presented here, we identified genome-scale POB-induced mutational signatures in cell lines and rat tumors, while also investigating their role in human cancer. These signatures are characterized by T>N and C>T mutations forming from specific POB adducts damaging dT and dC residues. Analysis of 2,780 cancer genomes uncovered POB signatures in [~]180 tumors; from cancer types distinct from the ones linked to smoking-related signatures SBS4 and SBS92. This suggests that, unlike PAH compounds, the POB pathway may contribute uniquely to the mutational landscapes of certain hematological malignancies and cancers of the kidney, breast, prostate and pancreas.

genomics↗

bioRxiv2023

Assigning mutational signatures to individual samples and individual somatic mutations with SigProfilerAssignment

Analysis of mutational signatures is a powerful approach for understanding the mutagenic processes that have shaped the evolution of a cancer genome. Here we present SigProfilerAssignment, a desktop and an online computational framework for assigning all types of mutational signatures to individual samples. SigProfilerAssignment is the first tool that allows both analysis of copy-number signatures and probabilistic assignment of signatures to individual somatic mutations. As its computational engine, the tool uses a custom implementation of the forward stagewise algorithm for sparse regression and nonnegative least squares for numerical optimization. Analysis of 2,700 synthetic cancer genomes with and without noise demonstrates that SigProfilerAssignment outperforms four commonly used approaches for assigning mutational signatures. SigProfilerAssignment is freely available at https://github.com/AlexandrovLab/SigProfilerAssignment with a web implementation at https://cancer.sanger.ac.uk/signatures/assignment/.

bioinformatics↗

bioRxiv2020

MSA: Reproducible mutational signature attribution with confidence based on simulations

BackgroundMutational signatures proved to be a useful tool for identifying patterns of mutations in genomes, often providing valuable insights about mutagenic processes or normal DNA damage. De novo extraction of signatures is commonly performed using Non-Negative Matrix Factorisation (NMF) methods, however, accurate attribution of these signatures to individual samples is a distinct problem requiring uncertainty estimation, particularly in noisy scenarios or when the acting signatures have similar shapes. Whilst many packages for signature attribution exist, a few provide accuracy measures, and most are not easily reproducible nor scalable in high-performance computing environments. ResultsWe present MSA (Mutational Signature Attribution), a reproducible pipeline designed to assign signatures of different mutation types on a single-sample basis, based on Non-Negative Least Squares (NNLS) method with optimisation. Parametric bootstrap is proposed as a way to measure statistical uncertainties of signature attribution. Supported mutation types include single and doublet base substitutions, indels and structural variants. Results are validated using simulations with reference COSMIC signatures, as well as randomly generated signatures. Availability and implementationMSA comprises a set of Python scripts unified in a single Nextflow pipeline with containerisation for cross-platform reproducibility and scalability in high-performance computing environments. The tool is publicly available from https://gitlab.com/s.senkin/MSA

bioinformatics↗

bioRxiv2020

Uncovering novel mutational signatures by de novo extraction with SigProfilerExtractor

Mutational signature analysis is commonly performed in genomic studies surveying cancer and normal somatic tissues. Here we present SigProfilerExtractor, an automated tool for accurate de novo extraction of mutational signatures for all types of somatic mutations. Benchmarking with a total of 34 distinct scenarios encompassing 2,500 simulated signatures operative in more than 60,000 unique synthetic genomes and 20,000 synthetic exomes demonstrates that SigProfilerExtractor outperforms thirteen other tools across all datasets with and without noise. For genome simulations with 5% noise, reflecting high-quality genomic datasets, SigProfilerExtractor outperforms other approaches by elucidating between 20% and 50% more true positive signatures while yielding more than 5-fold less false positive signatures. Applying SigProfilerExtractor to 4,643 whole-genome sequenced and 19,184 whole-exome sequenced cancers reveals four previously missed mutational signatures. Two of the signatures are confirmed in independent cohorts with one of these signatures associating with tobacco smoking. In summary, this report provides a reference tool for analysis of mutational signatures, a comprehensive benchmarking of bioinformatics tools for extracting mutational signatures, and several novel mutational signatures including a signature putatively attributed to direct tobacco smoking mutagenesis in bladder cancer and in normal bladder epithelium.

bioinformatics↗