Kim Yoo-Ah, Madan Sanna, Przytycka Teresa M
NCBI, NLM, NIH, Bethesda, MD, 20894, USA.
Poolesville High School, Poolesville, 20837 MD, USA.
Bioinformatics. 2017 Mar 15;33(6):814-821. doi: 10.1093/bioinformatics/btw242.
Mutual exclusivity is a widely recognized property of many cancer drivers. Knowledge about these relationships can provide important insights into cancer drivers, cancer-driving pathways and cancer subtypes. It can also be used to predict new functional interactions between cancer driving genes and uncover novel cancer drivers. Currently, most of mutual exclusivity analyses are preformed focusing on a limited set of genes in part due to the computational cost required to rigorously compute P -values.
To reduce the computing cost and perform less restricted mutual exclusivity analysis, we developed an efficient method to estimate P -values while controlling the mutation rates of individual patients and genes similar to the permutation test. A comprehensive mutual exclusivity analysis allowed us to uncover mutually exclusive pairs, some of which may have relatively low mutation rates. These pairs often included likely cancer drivers that have been missed in previous analyses. More importantly, our results demonstrated that mutual exclusivity can also provide information that goes beyond the interactions between cancer drivers and can, for example, elucidate different mutagenic processes in different cancer groups. In particular, including frequently mutated, long genes such as TTN in our analysis allowed us to observe interesting patterns of APOBEC activity in breast cancer and identify a set of related driver genes that are highly predictive of patient survival. In addition, we utilized our mutual exclusivity analysis in support of a previously proposed model where APOBEC activity is the underlying process that causes TP53 mutations in a subset of breast cancer cases.
http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#wesme.
Supplementary data are available at Bioinformatics online.
互斥性是许多癌症驱动因素广泛认可的特性。了解这些关系可为癌症驱动因素、癌症驱动途径和癌症亚型提供重要见解。它还可用于预测癌症驱动基因之间的新功能相互作用并发现新的癌症驱动因素。目前,由于严格计算P值所需的计算成本,大多数互斥性分析都集中在有限的一组基因上。
为了降低计算成本并进行限制较少的互斥性分析,我们开发了一种有效的方法来估计P值,同时控制个体患者和基因的突变率,类似于排列检验。全面的互斥性分析使我们能够发现互斥对,其中一些可能具有相对较低的突变率。这些对通常包括先前分析中遗漏的可能的癌症驱动因素。更重要的是,我们的结果表明,互斥性还可以提供超出癌症驱动因素之间相互作用的信息,例如,可以阐明不同癌症组中的不同诱变过程。特别是,在我们的分析中纳入经常突变的长基因,如TTN,使我们能够观察到乳腺癌中APOBEC活性的有趣模式,并识别出一组高度预测患者生存的相关驱动基因。此外,我们利用互斥性分析来支持先前提出的模型,其中APOBEC活性是导致一部分乳腺癌病例中TP53突变的潜在过程。
http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#wesme。
补充数据可在《生物信息学》在线获取。