Supporting data for "Combinatorial Detection of Conserved Alteration Patterns for Identifying Cancer Subnetworks"

Advances in large scale tumor sequencing have lead to an understanding that there are combinations of genomic and transcriptomic alterations specific to tumor types, shared across many patients. Unfortunately, computational identi¬cation of functionally meaningful and recurrent alteration patterns within gene/protein interaction networks has proven to be challenging. We introduce a novel combinatorial method, cd-CAP, for simultaneous detection of connected subnetworks of an interaction network where genes exhibit conserved alteration patterns across tumor samples. Our method differentiates distinct alteration types associated with each gene (rather than relying on binary information of a gene being altered or not), and simultaneously detects multiple alteration profile conserved subnetworks. In a number of The Cancer Genome Atlas (TCGA) data sets, cd-CAP identi¬fied large biologically signi¬cant subnetworks with conserved alteration patterns, shared across many tumor samples.随着大规模肿瘤测序技术的发展，学界已明确：存在特定于肿瘤类型的基因组与转录组改变组合，且该类组合在众多患者中共享。然而，在基因/蛋白质相互作用网络中，对具有功能意义且反复出现的改变模式进行计算识别，已被证实颇具挑战。本研究提出一种全新的组合学方法cd-CAP，用于同时检测相互作用网络中满足以下条件的连通子网络：网络内的基因在不同肿瘤样本间呈现保守的改变模式。该方法能够区分与每个基因相关的不同改变类型（而非仅依赖基因是否发生改变的二元信息），同时可检测出多个携带保守改变特征的子网络。在多个癌症基因组图谱（The Cancer Genome Atlas, TCGA）数据集的测试中，cd-CAP成功识别出了大量具有显著生物学意义、且在众多肿瘤样本中共享保守改变模式的子网络。