Institut Curie, PSL Research University, Translational Research Department, INSERM, U932 Immunity and Cancer, Residual Tumor & Response to Treatment Laboratory (RT2Lab), Paris, France.
MINES ParisTech, PSL University, CBIO - Centre for Computational Biology, Paris, France.
Nat Commun. 2021 Sep 9;12(1):5352. doi: 10.1038/s41467-021-24992-y.
Systematic DNA sequencing of cancer samples has highlighted the importance of two aspects of cancer genomics: intra-tumor heterogeneity (ITH) and mutational processes. These two aspects may not always be independent, as different mutational processes could be involved in different stages or regions of the tumor, but existing computational approaches to study them largely ignore this potential dependency. Here, we present CloneSig, a computational method to jointly infer ITH and mutational processes in a tumor from bulk-sequencing data. Extensive simulations show that CloneSig outperforms current methods for ITH inference and detection of mutational processes when the distribution of mutational signatures changes between clones. Applied to a large cohort of 8,951 tumors with whole-exome sequencing data from The Cancer Genome Atlas, and on a pan-cancer dataset of 2,632 whole-genome sequencing tumor samples from the Pan-Cancer Analysis of Whole Genomes initiative, CloneSig obtains results overall coherent with previous studies.
癌症样本的系统 DNA 测序强调了癌症基因组学两个方面的重要性：肿瘤内异质性（ITH）和突变过程。这两个方面并不总是相互独立的，因为不同的突变过程可能涉及肿瘤的不同阶段或区域，但现有的计算方法在很大程度上忽略了这种潜在的依赖性。在这里，我们提出了 CloneSig，这是一种从批量测序数据中联合推断肿瘤 ITH 和突变过程的计算方法。广泛的模拟表明，当克隆之间的突变特征分布发生变化时，CloneSig 在 ITH 推断和突变过程检测方面优于当前的方法。将 CloneSig 应用于来自癌症基因组图谱的 8951 个具有全外显子组测序数据的大型队列，以及来自 Pan-Cancer Analysis of Whole Genomes 计划的 2632 个全基因组测序肿瘤样本的泛癌数据集，CloneSig 的结果总体上与先前的研究一致。
