Analysis of mutational signatures with yet another package for signature analysis. Issue 5 (31st December 2020)

• Record Type: Journal Article
• Authors: Hübschmann, Daniel; Jopp‐Saile, Lea; Andresen, Carolin; Krämer, Stephen; Gu, Zuguang; Heilig, Christoph E.; Kreutzfeldt, Simon; Teleanu, Veronica; Fröhling, Stefan; Eils, Roland; Schlesner, Matthias
• Other Names: Stenzinger Albrecht guestEditor.; Gonzalez David guestEditor.
• Is Part Of: Genes, chromosomes & cancer. Volume 60:Issue 5(2021)
• Page Start: 314
• Page End: 331
• Publication Date: 2020-12-31
• Subjects: biomarker -- BRCAness -- cancer genomics -- mutational signatures -- precision oncology -- YAPSA; Cancer -- Genetic aspects -- Periodicals; 616.994042
• DOI: 10.1002/gcc.22918
• Languages: English
• ISSNs: 1045-2257

Abstract

Abstract: Different mutational processes leave characteristic patterns of somatic mutations in the genome that can be identified as mutational signatures. Determining the contributions of mutational signatures to cancer genomes allows not only to reconstruct the etiology of somatic mutations, but can also be used for improved tumor classification and support therapeutic decisions. We here present the R package yet another package for signature analysis (YAPSA) to deconvolute the contributions of mutational signatures to tumor genomes. YAPSA provides in‐built collections from the COSMIC and PCAWG SNV signature sets as well as the PCAWG Indel signatures and employs signature‐specific cutoffs to increase sensitivity and specificity. Furthermore, YAPSA allows to determine 95% confidence intervals for signature exposures, to perform constrained stratified signature analyses to obtain enrichment and depletion patterns of the identified signatures and, when applied to whole exome sequencing data, to correct for the triplet content of individual target capture kits. With this functionality, YAPSA has proved to be a valuable tool for analysis of mutational signatures in molecular tumor boards in a precision oncology context. YAPSA is available at R/Bioconductor (http://bioconductor.org/packages/3.12/bioc/html/YAPSA.html ).