Identification of ovarian cancer subtype-specific network modules and candidate drivers through an integrative genomics approach.
Identification of ovarian cancer subtype-specific network modules and candidate drivers through an integrative genomics approach.
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通过综合基因组学方法识别卵巢癌亚型特异性网络模块和候选驱动因素
DOI：
10.18632/oncotarget.6774
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发表时间：
2016-01-26
期刊：
影响因子：
--
通讯作者：
Xia J
中科院分区：
文献类型：
--
作者：
Zhang D;Chen P;Zheng CH;Xia J
识别癌症亚型和相关分子驱动因素对于理解肿瘤异质性和寻求有效的临床治疗至关重要。在这项研究中，我们引入了一个简单但有效的多步骤程序来定义卵巢癌类型，并通过整合多个数据源（包括mRNA表达，microRNA表达，拷贝数变异和蛋白质-蛋白质相互作用数据）来识别每个亚型的核心网络/通路和驱动基因。应用相似性网络融合方法对379例卵巢癌患者队列进行研究，发现两种不同的综合癌症亚型具有不同的生存特征。对于每种卵巢癌亚型，我们通过使用基于网络的方法探索了候选致癌过程和驱动基因。我们的分析表明，DLST模块参与的代谢途径和NDRG 1模块的改变是共同的两个亚型。然而，RB信号通路的改变在不同的卵巢癌亚型中驱动不同的分子和临床表型。这项研究提供了一个计算框架，利用大规模基因组数据的全部潜力来发现卵巢癌亚型特异性网络模块和候选驱动因素。该框架也可用于确定卵巢癌亚组中的新治疗靶点，目前存在有限的治疗机会。
Identification of cancer subtypes and associated molecular drivers is critically important for understanding tumor heterogeneity and seeking effective clinical treatment. In this study, we introduced a simple but efficient multistep procedure to define ovarian cancer types and identify core networks/pathways and driver genes for each subtype by integrating multiple data sources, including mRNA expression, microRNA expression, copy number variation, and protein-protein interaction data. Applying similarity network fusion approach to a patient cohort with 379 ovarian cancer samples, we found two distinct integrated cancer subtypes with different survival profiles. For each ovarian cancer subtype, we explored the candidate oncogenic processes and driver genes by using a network-based approach. Our analysis revealed that alterations in DLST module involved in metabolism pathway and NDRG1 module were common between the two subtypes. However, alterations in the RB signaling pathway drove distinct molecular and clinical phenotypes in different ovarian cancer subtypes. This study provides a computational framework to harness the full potential of large-scale genomic data for discovering ovarian cancer subtype-specific network modules and candidate drivers. The framework may also be used to identify new therapeutic targets in a subset of ovarian cancers, for which limited therapeutic opportunities currently exist.