Gao Bo, Zhao Yue, Li Yang, Liu Juntao, Wang Lushan, Li Guojun, Su Zhengchang
School of Mathematics Shandong University Jinan 250100 China.
State Key Laboratory of Microbial Technology Shandong University Jinan 250100 China.
Adv Sci (Weinh). 2018 Dec 18;6(4):1801384. doi: 10.1002/advs.201801384. eCollection 2019 Feb 20.
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/.
癌症驱动突变的互斥性是癌症突变图谱中经常观察到的一种现象。罕见突变的长尾使得互斥驱动模块的发现变得复杂。现有方法通常存在这样的问题，即一些已识别模块中只有少数基因覆盖了大多数癌症样本。为了克服这一障碍，通过识别具有平衡排他性覆盖的互斥驱动模块，提出了一种有效的方法UniCovEx。UniCovEx首先使用贪婪策略在信号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范围、排他性和覆盖平衡来评估它们，使用一种称为模块排他熵的新指标，该指标衡量模块的平衡程度。最后，UniCovEx通过使用贪婪策略解决最小集覆盖问题来预测样本特异性驱动模块。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显示出比以前的方法具有显著优势。该软件可在以下网址获得：https://sourceforge.net/projects/cancer-pathway/files/ 。
