Simultaneous identification of multiple driver pathways in cancer.
Simultaneous identification of multiple driver pathways in cancer.
复制标题
DOI：
10.1371/journal.pcbi.1003054
复制
发表时间：
2013
影响因子：
4.3
通讯作者：
Raphael BJ
中科院分区：
文献类型：
--
作者：
Leiserson MD;Blokh D;Sharan R;Raphael BJ
区分导致癌症的体细胞突变（驱动突变）与随机的乘客突变是癌症基因组学的一个关键挑战。驱动突变通常针对由多个基因组成的细胞信号传导和调控途径。由于在不同的样本中观察到驱动通路中不同的突变组合，这种异质性使驱动突变的识别变得复杂。我们介绍了Multi-Dendrix算法，用于同时识别来自癌症样本队列的体细胞突变数据中的多个驱动通路。该算法依赖于驱动路径中突变的两个组合特性：高覆盖率和互斥性。我们推导了一个整数线性程序，它找到了一组具有这些性质的突变。我们将Multi-Dendrix应用于胶质母细胞瘤、乳腺癌和肺癌样本的体细胞突变。Multi-Dendrix识别出与已知途径重叠的基因突变集，包括Rb、p53、PI(3)K和细胞周期途径，以及新的互斥突变集，包括几个转录因子或其他参与转录调节的基因的突变。这些集合是直接从突变数据中发现的，没有途径或基因相互作用的先验知识。我们表明，Multi-Dendrix在识别突变组合方面优于其他算法，并且在基因组规模数据上也快了几个数量级。软件可在：http://compbio.cs.brown.edu/software。癌症是一种主要由个体一生中体细胞突变积累引起的疾病。随着基因组测序成本的下降，现在可以测量数百种癌症基因组的体细胞突变。一个关键的挑战是区分导致癌症的驱动突变和随机的乘客突变。这一挑战由于在同一癌症类型的不同患者中观察到不同的驱动突变组合而变得更加复杂。这种异质性的一个原因是驱动突变的目标信号和调控途径有多个失败点。我们引入了一种算法，Multi-Dendrix，从患者队列中突变之间的相互排他性模式中找到这些通路。与早期的方法不同，我们同时发现了多种途径，这是分析多种途径通常受到干扰的癌症基因组的基本特征。我们将我们的算法应用于数百名胶质母细胞瘤、乳腺癌和肺腺癌患者的突变数据。我们确定了重叠已知途径的相互作用基因集，以及包含亚型特异性突变的基因集。这些结果表明，多种癌症途径可以直接从突变数据模式中识别出来，并为分析不断增长的癌症突变数据集提供了一种方法。
Distinguishing the somatic mutations responsible for cancer (driver mutations) from random, passenger mutations is a key challenge in cancer genomics. Driver mutations generally target cellular signaling and regulatory pathways consisting of multiple genes. This heterogeneity complicates the identification of driver mutations by their recurrence across samples, as different combinations of mutations in driver pathways are observed in different samples. We introduce the Multi-Dendrix algorithm for the simultaneous identification of multiple driver pathways de novo in somatic mutation data from a cohort of cancer samples. The algorithm relies on two combinatorial properties of mutations in a driver pathway: high coverage and mutual exclusivity. We derive an integer linear program that finds set of mutations exhibiting these properties. We apply Multi-Dendrix to somatic mutations from glioblastoma, breast cancer, and lung cancer samples. Multi-Dendrix identifies sets of mutations in genes that overlap with known pathways – including Rb, p53, PI(3)K, and cell cycle pathways – and also novel sets of mutually exclusive mutations, including mutations in several transcription factors or other genes involved in transcriptional regulation. These sets are discovered directly from mutation data with no prior knowledge of pathways or gene interactions. We show that Multi-Dendrix outperforms other algorithms for identifying combinations of mutations and is also orders of magnitude faster on genome-scale data. Software available at: http://compbio.cs.brown.edu/software. Cancer is a disease driven largely by the accumulation of somatic mutations during the lifetime of an individual. The declining costs of genome sequencing now permit the measurement of somatic mutations in hundreds of cancer genomes. A key challenge is to distinguish driver mutations responsible for cancer from random passenger mutations. This challenge is compounded by the observation that different combinations of driver mutations are observed in different patients with the same cancer type. One reason for this heterogeneity is that driver mutations target signaling and regulatory pathways which have multiple points of failure. We introduce an algorithm, Multi-Dendrix, to find these pathways solely from patterns of mutual exclusivity between mutations across a cohort of patients. Unlike earlier approaches, we simultaneously find multiple pathways, an essential feature for analyzing cancer genomes where multiple pathways are typically perturbed. We apply our algorithm to mutation data from hundreds of glioblastoma, breast cancer, and lung adenocarcinoma patients. We identify sets of interacting genes that overlap known pathways, and gene sets containing subtype-specific mutations. These results show that multiple cancer pathways can be identified directly from patterns in mutation data, and provide an approach to analyze the ever-growing cancer mutation datasets.
登录
查看更多内容
影响因子：
14.9
作者：
Gonzalez-Perez A;Lopez-Bigas N
通讯作者：
Lopez-Bigas N
影响因子：
14.9
作者：
Kanehisa M;Goto S;Sato Y;Furumichi M;Tanabe M
通讯作者：
Tanabe M
精神分裂症，躁郁症和抑郁症的跨disor阶层基因组分析。
DOI：
10.1176/appi.ajp.2010.09091335
发表时间：
2010-10
期刊：
The American journal of psychiatry
影响因子：
--
作者：
Huang J;Perlis RH;Lee PH;Rush AJ;Fava M;Sachs GS;Lieberman J;Hamilton SP;Sullivan P;Sklar P;Purcell S;Smoller JW
通讯作者：
Smoller JW
影响因子：
3.7
作者：
Cerami E;Demir E;Schultz N;Taylor BS;Sander C
通讯作者：
Sander C
影响因子：
14.8
作者：
Kumar, Prateek;Henikoff, Steven;Ng, Pauline C.
通讯作者：
Ng, Pauline C.