MEMCover: Integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types

83Citations
Citations of this article
97Readers
Mendeley users who have this article in their library.

This article is free to access.

Abstract

Motivation: The data gathered by the Pan-Cancer initiative has created an unprecedented opportunity for illuminating common features across different cancer types. However, separating tissue-specific features from across cancer signatures has proven to be challenging. One of the often-observed properties of the mutational landscape of cancer is the mutual exclusivity of cancer driving mutations. Even though studies based on individual cancer types suggested that mutually exclusive pairs often share the same functional pathway, the relationship between across cancer mutual exclusivity and functional connectivity has not been previously investigated. Results: We introduce a classification of mutual exclusivity into three basic classes: within tissue type exclusivity, across tissue type exclusivity and between tissue type exclusivity. We then combined across-cancer mutual exclusivity with interactions data to uncover pan-cancer dysregulated pathways. Our new method, Mutual Exclusivity Module Cover (MEMCover) not only identified previously known Pan-Cancer dysregulated subnetworks but also novel subnetworks whose across cancer role has not been appreciated well before. In addition, we demonstrate the existence of mutual exclusivity hubs, putatively corresponding to cancer drivers with strong growth advantages. Finally, we show that while mutually exclusive pairs within or across cancer types are predominantly functionally interacting, the pairs in between cancer mutual exclusivity class are more often disconnected in functional networks.

Cite

CITATION STYLE

APA

Kim, Y. A., Cho, D. Y., Dao, P., & Przytycka, T. M. (2015). MEMCover: Integrated analysis of mutual exclusivity and functional network reveals dysregulated pathways across multiple cancer types. In Bioinformatics (Vol. 31, pp. i284–i292). Oxford University Press. https://doi.org/10.1093/bioinformatics/btv247

Register to see more suggestions

Mendeley helps you to discover research relevant for your work.

Already have an account?

Save time finding and organizing research with Mendeley

Sign up for free