Leiserson Mark D M, Vandin Fabio, Wu Hsin-Ta, Dobson Jason R, Eldridge Jonathan V, Thomas Jacob L, Papoutsaki Alexandra, Kim Younhun, Niu Beifang, McLellan Michael, Lawrence Michael S, Gonzalez-Perez Abel, Tamborero David, Cheng Yuwei, Ryslik Gregory A, Lopez-Bigas Nuria, Getz Gad, Ding Li, Raphael Benjamin J
1] Department of Computer Science, Brown University, Providence, Rhode Island, USA. [2] Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA.
1] Department of Computer Science, Brown University, Providence, Rhode Island, USA. [2] Center for Computational Molecular Biology, Brown University, Providence, Rhode Island, USA. [3] Department of Molecular Biology, Cell Biology and Biochemistry, Brown University, Providence, Rhode Island, USA.
Nat Genet. 2015 Feb;47(2):106-14. doi: 10.1038/ng.3168. Epub 2014 Dec 15.
Cancers exhibit extensive mutational heterogeneity, and the resulting long-tail phenomenon complicates the discovery of genes and pathways that are significantly mutated in cancer. We perform a pan-cancer analysis of mutated networks in 3,281 samples from 12 cancer types from The Cancer Genome Atlas (TCGA) using HotNet2, a new algorithm to find mutated subnetworks that overcomes the limitations of existing single-gene, pathway and network approaches. We identify 16 significantly mutated subnetworks that comprise well-known cancer signaling pathways as well as subnetworks with less characterized roles in cancer, including cohesin, condensin and others. Many of these subnetworks exhibit co-occurring mutations across samples. These subnetworks contain dozens of genes with rare somatic mutations across multiple cancers; many of these genes have additional evidence supporting a role in cancer. By illuminating these rare combinations of mutations, pan-cancer network analyses provide a roadmap to investigate new diagnostic and therapeutic opportunities across cancer types.
癌症表现出广泛的突变异质性，由此产生的长尾现象使在癌症中发生显著突变的基因和通路的发现变得复杂。我们使用HotNet2对来自癌症基因组图谱（TCGA）的12种癌症类型的3281个样本中的突变网络进行泛癌分析，HotNet2是一种用于发现突变子网的新算法，它克服了现有单基因、通路和网络方法的局限性。我们识别出16个显著突变的子网，其中包括著名的癌症信号通路以及在癌症中作用尚不明确的子网，包括黏连蛋白、凝聚素等。这些子网中的许多在样本间表现出共突变。这些子网包含数十个在多种癌症中发生罕见体细胞突变的基因；其中许多基因有更多证据支持其在癌症中的作用。通过揭示这些罕见的突变组合，泛癌网络分析为研究跨癌症类型的新诊断和治疗机会提供了路线图。
