CoMEt：一种识别癌症中互斥变异组合的统计方法
CoMEt: A Statistical Approach to Identify Combinations of Mutually Exclusive Alterations in Cancer
摘要：
癌症是一种异质性疾病，具有遗传和表观遗传改变的不同组合，驱动不同个体中癌症的发展。虽然这些改变被认为集中在关键细胞信号传导和调控途径中的基因上，但我们对这些途径的了解仍然不完整，因此很难通过它们在基因或已知途径中的复发来识别驱动改变。我们引入了互斥改变组合（CoMEt），这是一种识别从头改变组合的算法，无需任何先前的生物学知识（例如途径或蛋白质相互作用）。CoMEt搜索表现出互斥性的突变组合，这是一种预期的通路突变模式。CoMEt有几个重要的特点，区别于现有的方法来分析变化之间的互斥性。其中包括：互斥性的精确统计检验，其在检测包含罕见改变的组合时更敏感;同时鉴定一个或多个互斥改变组合的集合;同时分析亚型特异性突变;以及对互斥改变集合的集合进行汇总。这些功能使CoMEt能够稳健地识别影响多个通路或癌症标志的改变。我们表明，CoMEt优于现有的模拟和真实数据的方法。将CoMEt应用于来自TCGA的四种不同癌症类型的数百个样本，揭示了每种癌症类型中的多个互斥集。其中许多与已知的途径重叠，但另一些揭示了新的推定癌症基因。* 平等捐款。
英文摘要：
Cancer is a heterogeneous disease with different combinations of genetic and epigenetic alterations driving the development of cancer in different individuals. While these alterations are believed to converge on genes in key cellular signaling and regulatory pathways, our knowledge of these pathways remains incomplete, making it difficult to identify driver alterations by their recurrence across genes or known pathways. We introduce Combinations of Mutually Exclusive Alterations (CoMEt), an algorithm to identify combinations of alterations de novo, without any prior biological knowledge (e.g. pathways or protein interactions). CoMEt searches for combinations of mutations that exhibit mutual exclusivity, a pattern expected for mutations in pathways. CoMEt has several important feature that distinguish it from existing approaches to analyze mutual exclusivity among alterations. These include: an exact statistical test for mutual exclusivity that is more sensitive in detecting combinations containing rare alterations; simultaneous identification of collections of one or more combinations of mutually exclusive alterations; simultaneous analysis of subtype-specific mutations; and summarization over an ensemble of collections of mutually exclusive alterations. These features enable CoMEt to robustly identify alterations affecting multiple pathways, or hallmarks of cancer. We show that CoMEt outperforms existing approaches on simulated and real data. Application of CoMEt to hundreds of samples from four different cancer types from TCGA reveals multiple mutually exclusive sets within each cancer type. Many of these overlap known pathways, but others reveal novel putative cancer genes. *Equal contribution.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
