Amgalan Bayarbaatar, Lee Hyunju
School of Information and Communications, Gwangju Institute of Science and Technology, Gwangju, Republic of Korea.
Bioinformatics. 2015 Aug 1;31(15):2452-60. doi: 10.1093/bioinformatics/btv175. Epub 2015 Mar 26.
The generation of a large volume of cancer genomes has allowed us to identify disease-related alterations more accurately, which is expected to enhance our understanding regarding the mechanism of cancer development. With genomic alterations detected, one challenge is to pinpoint cancer-driver genes that cause functional abnormalities.
Here, we propose a method for uncovering the dominant effects of cancer-driver genes (DEOD) based on a partial covariance selection approach. Inspired by a convex optimization technique, it estimates the dominant effects of candidate cancer-driver genes on the expression level changes of their target genes. It constructs a gene network as a directed-weighted graph by integrating DNA copy numbers, single nucleotide mutations and gene expressions from matched tumor samples, and estimates partial covariances between driver genes and their target genes. Then, a scoring function to measure the cancer-driver score for each gene is applied. To test the performance of DEOD, a novel scheme is designed for simulating conditional multivariate normal variables (targets and free genes) given a group of variables (driver genes). When we applied the DEOD method to both the simulated data and breast cancer data, DEOD successfully uncovered driver variables in the simulation data, and identified well-known oncogenes in breast cancer. In addition, two highly ranked genes by DEOD were related to survival time. The copy number amplifications of MYC (8q24.21) and TRPS1 (8q23.3) were closely related to the survival time with P-values = 0.00246 and 0.00092, respectively. The results demonstrate that DEOD can efficiently uncover cancer-driver genes.
大量癌症基因组的产生使我们能够更准确地识别与疾病相关的改变，这有望增进我们对癌症发展机制的理解。随着基因组改变的检测，一个挑战是找出导致功能异常的癌症驱动基因。
在此，我们提出一种基于偏协方差选择方法来揭示癌症驱动基因显性效应（DEOD）的方法。受凸优化技术启发，它估计候选癌症驱动基因对其靶基因表达水平变化的显性效应。通过整合来自匹配肿瘤样本的DNA拷贝数、单核苷酸突变和基因表达构建一个基因网络作为有向加权图，并估计驱动基因与其靶基因之间的偏协方差。然后，应用一个评分函数来衡量每个基因的癌症驱动得分。为了测试DEOD的性能，设计了一种新方案来模拟给定一组变量（驱动基因）时的条件多元正态变量（靶标和自由基因）。当我们将DEOD方法应用于模拟数据和乳腺癌数据时，DEOD成功地在模拟数据中揭示了驱动变量，并在乳腺癌中识别出了知名的癌基因。此外，DEOD排名靠前的两个基因与生存时间相关。MYC（8q24.21）和TRPS1（8q23.3）的拷贝数扩增与生存时间密切相关，P值分别为0.00246和0.00092。结果表明，DEOD能够有效地揭示癌症驱动基因。
