Computational Oncology, Molecular Diagnostics Program, National Center for Tumor Diseases (NCT) and German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.
Pattern Recognition and Digital Medicine, Heidelberg Institute of Stem Cell Technology and Experimental Medicine (HI-STEM), Im Neuenheimer Feld 280, Heidelberg, 69120, Germany.
Genes Chromosomes Cancer. 2021 May;60(5):314-331. doi: 10.1002/gcc.22918. Epub 2020 Dec 31.
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).
不同的突变过程会在基因组中留下特征性的体细胞突变模式,这些模式可以被识别为突变特征。确定突变特征对癌症基因组的贡献不仅可以重建体细胞突变的病因,还可以用于改进肿瘤分类和支持治疗决策。我们在这里介绍 R 包 yet another package for signature analysis (YAPSA),用于解析突变特征对肿瘤基因组的贡献。YAPSA 提供了来自 COSMIC 和 PCAWG SNV 特征集以及 PCAWG Indel 特征的内置集合,并采用特征特异性截止值来提高敏感性和特异性。此外,YAPSA 允许确定特征暴露的 95%置信区间,执行受限分层特征分析以获得已识别特征的富集和耗尽模式,并且当应用于全外显子组测序数据时,可以校正个体靶标捕获试剂盒的三联体含量。通过这些功能,YAPSA 已被证明是在精准肿瘤学背景下进行分子肿瘤委员会突变特征分析的有价值的工具。YAPSA 可在 R/Bioconductor(http://bioconductor.org/packages/3.12/bioc/html/YAPSA.html)上获得。