A Network-Centric Framework for the Evaluation of Mutual Exclusivity Tests on Cancer Drivers.

Rafsan Ahmed,Cansu Yalcin,Hilal Kazan,Aissa Houdjedj,Cesim Erten

doi:10.3389/fgene.2021.746495

Abstract

One of the key concepts employed in cancer driver gene identification is that of mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway. Several ME tests have been proposed recently, however the current protocols to evaluate ME tests have two main limitations. Firstly the evaluations are mostly with respect to simulated data and secondly the evaluation metrics lack a network-centric view. The latter is especially crucial as the notion of common functionality can be achieved through searching for interaction patterns in relevant networks. We propose a network-centric framework to evaluate the pairwise significances found by statistical ME tests. It has three main components. The first component consists of metrics employed in the network-centric ME evaluations. Such metrics are designed so that network knowledge and the reference set of known cancer genes are incorporated in ME evaluations under a careful definition of proper control groups. The other two components are designed as further mechanisms to avoid confounders inherent in ME detection on top of the network-centric view. To this end, our second objective is to dissect the side effects caused by mutation load artifacts where mutations driving tumor subtypes with low mutation load might be incorrectly diagnosed as mutually exclusive. Finally, as part of the third main component, the confounding issue stemming from the use of nonspecific interaction networks generated as combinations of interactions from different tissues is resolved through the creation and use of tissue-specific networks in the proposed framework. The data, the source code and useful scripts are available at: https://github.com/abu-compbio/NetCentric.

Highlights

Cancer is a disease caused mostly due to a gradual accumulation of somatic alterations that give rise to pathway dysregulation through alterations in copy number, DNA methylation, gene expression, and molecular function
A phenomenon observed frequently in the data pertaining to the alterations that the tumors acquire is mutual exclusivity (ME); a driver mutation is less likely to occur in case of an earlier mutation that has common functionality in the same molecular pathway (Thomas et al, 2007; Yeang et al, 2008; Leiserson et al, 2016; van de Haar et al, 2019)
Based on the premise that cancer driver genes interacting in the ProteinProtein Interaction (PPI) network are likely to exhibit ME, a pair gi, gj belongs to the set of True Positives if pi,j is significant and a pair gi, gr belongs to the set of False Positives if pi,r is significant

Summary

Introduction

Cancer is a disease caused mostly due to a gradual accumulation of somatic alterations that give rise to pathway dysregulation through alterations in copy number, DNA methylation, gene expression, and molecular function. Several driver gene or module identification approaches employ ME detection as part of their problem definitions and optimization goals (Babur et al, 2015; Ciriello et al, 2012; Leiserson et al, 2013; Kim et al, 2015; Ahmed et al, 2019; Baali et al, 2020). Such a central role in driver gene and module identification has led to the design of many different approaches for defining and computing mutual exclusivity. The focus of the proposed framework is the evaluation of the latter set of approaches consisting of the statistical ME tests

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Conclusion