Detection of candidate tumor driver genes using a fully integrated Bayesian approach.
Detection of candidate tumor driver genes using a fully integrated Bayesian approach.
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DOI：
10.1002/sim.6066
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发表时间：
2014-05-10
影响因子：
2
通讯作者：
Xiao, Guanghua
中科院分区：
文献类型：
--
作者：
Yang, Jichen;Wang, Xinlei;Kim, Minsoo;Xie, Yang;Xiao, Guanghua
DNA拷贝数改变（DNA copy number alterations，CNA）包括扩增和缺失，可导致基因表达的显著变化，与许多疾病尤其是癌症的发生和发展密切相关。例如，某些基因（称为候选肿瘤驱动基因）中与CNA相关的表达变化可以通过转录调控改变许多下游基因的表达水平，并导致癌症。这种候选肿瘤驱动基因的鉴定导致发现用于癌症的个性化治疗的新的治疗靶标。为此目的，已经开发了几种方法，通过使用拷贝数和基因表达数据。在这项研究中，我们提出了一种贝叶斯方法来识别候选肿瘤驱动基因，其中拷贝数和基因表达数据一起建模，两种数据类型之间的依赖关系通过条件概率建模。所提出的联合建模方法可以同时识别CNA和差异表达（DE）基因，从而改善候选肿瘤驱动基因的检测和对潜在生物过程的全面理解。该方法进行了评估，在模拟研究，然后应用到头颈部鳞状细胞癌（HNSCC）数据集。仿真研究和数据应用表明，联合建模方法可以显着提高识别候选肿瘤驱动基因的性能，当与其他现有的方法相比。
DNA copy number alterations (CNAs), including amplifications and deletions, can result in significant changes in gene expression, and are closely related to the development and progression of many diseases, especially cancer. For example, CNA-associated expression changes in certain genes (called candidate tumor driver genes) can alter the expression levels of many downstream genes through transcription regulation, and cause cancer. Identification of such candidate tumor driver genes leads to discovery of novel therapeutic targets for personalized treatment of cancers. Several approaches have been developed for this purpose by using both copy number and gene expression data. In this study, we propose a Bayesian approach to identify candidate tumor driver genes, in which the copy number and gene expression data are modeled together, and the dependency between the two data types is modeled through conditional probabilities. The proposed joint modeling approach can identify CNA and differentially expressed (DE) genes simultaneously, leading to improved detection of candidate tumor driver genes and comprehensive understanding of underlying biological processes. The proposed method was evaluated in simulation studies, and then applied to a head and neck squamous cell carcinoma (HNSCC) dataset. Both simulation studies and data application show that the joint modeling approach can significantly improve the performance in identifying candidate tumor driver genes, when compared to other existing approaches.
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影响因子：
2.2
作者：
Peel, D;McLachlan, GJ
通讯作者：
McLachlan, GJ
影响因子：
1.7
作者：
Choi, Hyungwon;Qin, Zhaohui S.;Ghosh, Debashis
通讯作者：
Ghosh, Debashis
影响因子：
2.1
作者：
Ma, Shuangge;Huang, Jian;Song, Xiao
通讯作者：
Song, Xiao
影响因子：
2.1
作者：
Olshen, AB;Venkatraman, ES;Wigler, M
通讯作者：
Wigler, M
影响因子：
64.8
作者：
Krishnamurthy, Janakiraman;Ramsey, Matthew R.;Sharpless, Norman E.
通讯作者：
Sharpless, Norman E.