Ciriello Giovanni, Cerami Ethan, Aksoy Bulent Arman, Sander Chris, Schultz Nikolaus
Computational Biology Center, Memorial Sloan-Kettering Cancer Center, New York, New York.
Curr Protoc Bioinformatics. 2013 Mar;Chapter 8:8.17.1-8.17.12. doi: 10.1002/0471250953.bi0817s41.
Although individual tumors show surprisingly diverse genomic alterations, these events tend to occur in a limited number of pathways, and alterations that affect the same pathway tend to not co-occur in the same patient. While pathway analysis has been a powerful tool in cancer genomics, our knowledge of oncogenic pathway modules is incomplete. To systematically identify such modules, we have developed a novel method, Mutual Exclusivity Modules in Cancer (MEMo). The method searches and identifies modules characterized by three properties: (1) member genes are recurrently altered across a set of tumor samples; (2) member genes are known to or are likely to participate in the same biological process; and (3) alteration events within the modules are mutually exclusive. MEMo integrates multiple data types and maps genomic alterations to biological pathways. MEMo's mutual exclusivity uses a statistical model that preserves the number of alterations per gene and per sample. The MEMo software, source code and sample data sets are available for download at: http://cbio.mskcc.org/memo.
尽管单个肿瘤显示出惊人的多样基因组改变,但这些事件往往发生在有限数量的通路中,并且影响相同通路的改变往往不会在同一患者中同时出现。虽然通路分析在癌症基因组学中是一个强大的工具,但我们对致癌通路模块的了解并不完整。为了系统地识别此类模块,我们开发了一种新方法——癌症中的互斥模块(MEMo)。该方法搜索并识别具有三个特性的模块:(1)成员基因在一组肿瘤样本中反复发生改变;(2)成员基因已知或可能参与相同的生物学过程;(3)模块内的改变事件是互斥的。MEMo整合多种数据类型,并将基因组改变映射到生物学通路。MEMo的互斥性使用一种统计模型,该模型保留每个基因和每个样本的改变数量。MEMo软件、源代码和样本数据集可在以下网址下载:http://cbio.mskcc.org/memo 。