Mutation frequency and copy number alterations determine prognosis and metastatic tropism in 60.000 clinical cancer samples

The intricate interplay between somatic mutations and copy number alterations critically influences tumour evolution and patient prognosis. Traditional genomic studies often overlook this interplay by analysing these two biomarker types in isolation. We developed INCOMMON, a computational method to detect allele-specific copy number alterations from clinical targeted panels without matched normal, discover recurrent tumour-specific patterns of co-existing mutations and copy-number alterations, and stratify patients based on these composite genotypes for downstream analyses of survival, metastatic propensity and organotropism. The tool can be used as an open-source R package available at https://github.com/caravagnalab/INCOMMON, and a shiny application available at https://ncalonaci.shinyapps.io/incommon/. This repository contains all the scripts that we used to analyse PCAWG, TCGA, MSK-MetTropism and AACR GENIE-Dfci data, and all the relevant results in the form of data tables.体细胞突变 (somatic mutations) 与拷贝数变异 (copy number alterations) 之间的复杂相互作用，对肿瘤演化进程与患者预后结局具有关键调控作用。传统基因组学研究往往将这两类生物标志物分开独立分析，因而忽略了二者间的这种相互作用。本研究开发了INCOMMON，一种可从无配对正常样本的临床靶向测序面板中检测等位基因特异性拷贝数变异的计算方法，能够识别共存突变与拷贝数变异的复发性肿瘤特异性模式，并基于这些复合基因型对患者进行分层，以开展生存分析、转移倾向分析及器官亲嗜性分析等后续研究。该工具以两种形式提供：一是可在https://github.com/caravagnalab/INCOMMON获取的开源R软件包，二是可在https://ncalonaci.shinyapps.io/incommon/访问的Shiny应用。本仓库包含了我们用于分析PCAWG、TCGA、MSK-MetTropism及AACR GENIE-Dfci数据集的全部代码脚本，以及以数据表形式呈现的所有相关研究结果。