Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples
Prediction of Driver Modules via Balancing Exclusive Coverages of Mutations in Cancer Samples
复制标题
通过平衡癌症样本中突变的独家覆盖范围来预测驱动模块
DOI：
10.1002/advs.201801384
复制
发表时间：
2019-02-20
期刊：
影响因子：
15.1
通讯作者：
Su, Zhengchang
中科院分区：
文献类型：
--
作者：
Gao, Bo;Zhao, Yue;Su, Zhengchang
癌症驱动突变的互斥性是癌症突变景观中经常观察到的现象。罕见突变的长尾使得发现互斥驱动模块变得复杂。现有的方法通常存在这样一个问题，即只有一些已识别模块中的少数基因覆盖了大多数癌症样本。为了克服这一障碍，提出了一种有效的UniCovEx方法，该方法通过识别平衡独占覆盖率的互斥驱动模块来实现。UniCovEx首先使用贪婪策略在信令网络中搜索具有强拓扑关系的候选驱动模块。然后，它通过考虑候选模块的覆盖率、独占性和覆盖率平衡来评估候选模块，并使用一种称为模块独占熵的新度量，该度量度量模块的平衡程度。最后，UniCovEx通过使用贪心策略解决最小集覆盖问题来预测样本特定的驱动模块。在12个不同癌症类型的癌症基因组图谱数据集上进行测试时，UniCovEx比之前的方法显示出明显的优势。该软件可在：https://sourceforge.net/projects/cancer‐路径/文件/。
Mutual exclusivity of cancer driving mutations is a frequently observed phenomenon in the mutational landscape of cancer. The long tail of rare mutations complicates the discovery of mutually exclusive driver modules. The existing methods usually suffer from the problem that only few genes in some identified modules cover most of the cancer samples. To overcome this hurdle, an efficient method UniCovEx is presented via identifying mutually exclusive driver modules of balanced exclusive coverages. UniCovEx first searches for candidate driver modules with a strong topological relationship in signaling networks using a greedy strategy. It then evaluates the candidate modules by considering their coverage, exclusivity, and balance of coverage, using a novel metric termed exclusive entropy of modules, which measures how balanced the modules are. Finally, UniCovEx predicts sample‐specific driver modules by solving a minimum set cover problem using a greedy strategy. When tested on 12 The Cancer Genome Atlas datasets of different cancer types, UniCovEx shows a significant superiority over the previous methods. The software is available at: https://sourceforge.net/projects/cancer‐pathway/files/.