WeSME: Uncovering Mutual Exclusivity of Cancer Drivers and Beyond

📝 Abstract

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.

💡 Analysis

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.

📄 Content

1 WeSME: Uncovering Mutual Exclusivity of Cancer Drivers and Beyond

Yoo-Ah Kim1, Sanna Madan2, and Teresa M. Przytycka1 * 1National Center for Biotechnology Information, NLM, NIH, Bethesda, MD 20894 2 Poolesville High School, Poolesville, MD

*To whom correspondence should be addressed; przytyck@ncbi.nlm.nih.gov

Abstract

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.
Accessibility: http://www.ncbi.nlm.nih.gov/CBBresearch/Przytycka/index.cgi#wesme

1 INTRODUCTION Mutual exclusivity is a frequently observed property in two or more genes having mutations in cancer patients [1-8]. Mutually exclusive gene pairs (i.e., gene pairs of which simultaneous mutations in the same patients are less frequent than it is expected by chance) are observed within many cancer types, while some are common across multiple different cancer types [1]. The underlying cause of mutual exclusivity might vary depending on the context. In many cases, genes mutated in a mutually exclusive way have been found to be members of the same functional pathway. In such a case, the mutual exclusivity of their mutation pattern can be explained by the pathway centric view of cancer. Specifically, if two genes belong to the same cancer-driving pathway, then a mutation in just one of them might suffice to dysregulate the pathway and trigger cancer progression [2, 5, 7, 9-11]. In contrast, in a combined analysis of multiple cancer types, mutually exclusive aberrations might represent tissue specific cancer

2 drivers. In fact, the sets of mutually exclusive drivers obtained in Pan-Cancer analysis [12, 13] predominantly included the tissue type specific genes that, typically, do not share common pathways. Yet, even within the same tissue type, mutual exclusivity between genes from different pathways is not unusual [1]. This type of exclusivity can be explained, for example, by synthetic lethality where simultaneous mutations in both genes are detrimental for cancer progression [14, 15]. Another (not necessarily unrelated) possibility is that the mutually exclusive drivers define different cancer subtypes within the same tissue type. Finally, mutual exclusivity can also be observed between less frequently mutated cancer drivers suggesting more subtle relationships between them and their contribution to cancer progression. For example, previous research found that even if the exclusivity between ARID1B and KRAS is not significant in any of individual cancer types separately, the pair was shown to be mutually exclusive when analyzing all cancer types together as statistical power was gained [1].
Mutual exclusivity can potentially provide important information about cancer, including its alternative progression pathways and subtypes. It can also reveal important relationships between genes and pathways. However, most of the analyses of mutual exclusivity so far have been focused on limited subsets of genes that were selected based on mutation rate cut-off (typically 3% or 5% depending on datasets) or restricted by other criteria. One of the factors limiting the applications of mutual exclusivity analysis beyond restricted gene sets was the cost of computing rigorous p-values for all gene pairs of interest. Statistically, pairwise mutual exclusivity can be estimated by

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