Mutational signatures of colorectal cancers according to distinct computational workflows

Abstract Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 23...

Celý popis

Uloženo v:
Podrobná bibliografie
Vydáno v:Briefings in bioinformatics Ročník 25; číslo 4
Hlavní autoři: Battuello, Paolo, Corti, Giorgio, Bartolini, Alice, Lorenzato, Annalisa, Sogari, Alberto, Russo, Mariangela, Di Nicolantonio, Federica, Bardelli, Alberto, Crisafulli, Giovanni
Médium: Journal Article
Jazyk:angličtina
Vydáno: England Oxford University Press 23.05.2024
Oxford Publishing Limited (England)
Témata:
Algorithms
Cancer
Cell Line, Tumor
Colorectal cancer
Colorectal carcinoma
Colorectal Neoplasms - genetics
Colorectal Neoplasms - pathology
Computational Biology - methods
Computer applications
Datasets
Endometrial cancer
Exome Sequencing - methods
Female
Gene sequencing
Genomic analysis
Humans
Impact analysis
Lung cancer
Mismatch repair
Mutation
Organoids
Precision medicine
Signature analysis
Signatures
Software
Statistical analysis
Uterine cancer
Whole genome sequencing
Workflow
computational biology
precision oncology
colorectal cancer
benchmarking
mutational signatures
bioinformatics
genomics
ISSN:1467-5463, 1477-4054, 1477-4054
On-line přístup:Získat plný text
Tagy: Přidat tag
Žádné tagy, Buďte první, kdo vytvoří štítek k tomuto záznamu!
Popis
Shrnutí:Abstract Tumor mutational signatures have gained prominence in cancer research, yet the lack of standardized methods hinders reproducibility and robustness. Leveraging colorectal cancer (CRC) as a model, we explored the influence of computational parameters on mutational signature analyses across 230 CRC cell lines and 152 CRC patients. Results were validated in three independent datasets: 483 endometrial cancer patients stratified by mismatch repair (MMR) status, 35 lung cancer patients by smoking status and 12 patient-derived organoids (PDOs) annotated for colibactin exposure. Assessing various bioinformatic tools, reference datasets and input data sizes including whole genome sequencing, whole exome sequencing and a pan-cancer gene panel, we demonstrated significant variability in the results. We report that the use of distinct algorithms and references led to statistically different results, highlighting how arbitrary choices may induce variability in the mutational signature contributions. Furthermore, we found a differential contribution of mutational signatures between coding and intergenic regions and defined the minimum number of somatic variants required for reliable mutational signature assignment. To facilitate the identification of the most suitable workflows, we developed Comparative Mutational Signature analysis on Coding and Extragenic Regions (CoMSCER), a bioinformatic tool which allows researchers to easily perform comparative mutational signature analysis by coupling the results from several tools and public reference datasets and to assess mutational signature contributions in coding and non-coding genomic regions. In conclusion, our study provides a comparative framework to elucidate the impact of distinct computational workflows on mutational signatures.
Bibliografie:ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 14
content type line 23
ISSN:1467-5463
1477-4054
1477-4054
DOI:10.1093/bib/bbae249