识别癌症基因组之间的互斥性：发现遗传相互作用并揭示肿瘤易感性的计算方法

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Identifying mutual exclusivity across cancer genomes:

computational approaches to discover genetic

interaction and reveal tumor vulnerability

Yulan Deng*, Shangyi Luo*, Chunyu Deng*, Tao Luo, Wenkang Yin,

Hongyi Zhang, Yong Zhang, Xinxin Zhang, Yujia Lan, Yanyan Ping,

Yun Xiao and Xia Li

Corresponding authors: Xia Li, College of Bioinformatics Science and Technology, Harbin Medical University, Harbin, Heilongjiang 150081, China. Tel.: 86-

451-86615922; Fax: 86-451-86615922; E-mail: lixia@hrbmu.edu.cn; Yun Xiao, College of Bioinformatics Science and Technology, Harbin Medical University,

Harbin, Heilongjiang 150081, China. Tel.: 86-451-86615922; Fax: 86-451-86615922; E-mail: xiaoyun@ems.hrbmu.edu.cn

*These authors contributed equally to this work.

Abstract

Systematic sequencing of cancer genomes has revealed prevalent heterogeneity, with patients harboring various combina-

torial patterns of genetic alteration. In particular, a phenomenon that a group of genes exhibits mutually exclusive patterns

has been widespread across cancers, covering a broad spectrum of crucial cancer pathways. Recently, there is considerable

evidence showing that, mutual exclusivity reﬂects alternative functions in tumor initiation and progression, or suggests

adverse effects of their concurrence. Given its importance, numerous computational approaches have been proposed to

study mutual exclusivity using genomic proﬁles alone, or by integrating networks and phenotypes. Some of them have

been routinely used to explore genetic associations, which lead to a deeper understanding of carcinogenic mechanisms and

reveals unexpected tumor vulnerabilities. Here, we present an overview of mutual exclusivity from the perspective of

cancer genome. We describe the common hypothesis underlying mutual exclusivity, summarize the strategies for the

identiﬁcation of signiﬁcant mutually exclusive patterns, compare the performance of representative algorithms from

simulated data sets and discuss their common confounders.

Key words: genomics; mutual exclusivity; functional redundancy; synthetic lethality; integration analysis

Yulan Deng is a PhD student in College of Bioinformatics Science and Technology at Harbin Medical University.

Shangyi Luo is an MS student in College of Bioinformatics Science and Technology at Harbin Medical University.

Chunyu Deng is an MS student in College of Bioinformatics Science and Technology at Harbin Medical University.

Tao Luo is an MS student in College of Bioinformatics Science and Technology at Harbin Medical University.

Wenkang Yin is an MS student in College of Bioinformatics Science and Technology at Harbin Medical University.

Hongyi Zhang is a PhD student in College of Bioinformatics Science and Technology at Harbin Medical University.

Yong Zhang is an MS student in College of Bioinformatics Science and Technology at Harbin Medical University.

Xinxin Zhang is an MS student in College of Bioinformatics Science and Technology at Harbin Medical University.

Yujia Lan is a PhD student in College of Bioinformatics Science and Technology at Harbin Medical University.

Yanyan Ping is an associate professor in College of Bioinformatics Science and Technology at Harbin Medical University.

Yun Xiao is an associate professor in College of Bioinformatics Science and Technology at Harbin Medical University.

Xia Li is a professor in College of Bioinformatics Science and Technology at Harbin Medical University.

Submitted: 23 May 2017; Received (in revised form): 7 July 2017

Briefings in Bioinformatics, 2017, 1–13

doi: 10.1093/bib/bbx109

Paper

Introduction

Cancer is driven by accumulating somatic alterations during an

individual’s lifetime. Thanks to large-scale cancer sequencing

projects [1, 2], tens of thousands of tumor genomes have been

released, charting the landscapes of genetic alteration across

multiple cancers [1–7]. Some genes were found to contribute to

cancerogenesis through conferring cell growth advantage,

referred to as drivers [8–10]. However, patients seldom harbor

exactly the same driving genetic alterations, thus showing ex-

tensive genetic heterogeneity [11–14].

Mutual exclusivity refers to the phenomenon that genetic al-

teration of a group of genes does not tend to occur in the same

sample, which has been widely observed in different cancer co-

horts [15, 16]. More than one quarter of well-known cancer

genes are involved in mutual exclusivity (see Supplementary

Materials)[17], covering a broad spectrum of cancer pathways,

such as RTK/RAS/PI(3) K pathway, TP53 pathway and Wnt path-

way. For example, BRAF and NRAS, two members of MAPK path-

way, undergo genetic alterations in 40 and 25% of melanoma

patients, respectively, while only a few patients harbor both

genetic alterations [18]. They exhibit a significant mutual exclu-

sivity. Such mutual exclusivity between BRAF and NRAS has

also been reported in other cancer types, such as papillary thy-

roid carcinomas [19], myeloma [20] and colorectal cancer [21].

Similarly, KRAS and EGFR mutations are mutually exclusive in

lung adenocarcinoma, which, respectively, account for 30 and

15% lung adenocarcinoma patients [22]. Forced expression of

both oncogenic mutations causes a proliferation and/or survival

disadvantage in cancer cells [23].

Therefore, several computational approaches are developed

to investigate mutually exclusive patterns in cancer genome.

Toward a better understanding of mutual exclusivity and asso-

ciated approaches, we present an overview of mutual exclusiv-

ity from the perspective of cancer genome. First of all, we briefly

describe possible hypotheses underlying mutual exclusivity.

Then, we overview strategies that have been adopted to identify

mutually exclusive modules, and compare the performance of

representative algorithms from simulated data sets at different

parameters. Finally, we comment on common confounders,

and provide recommendations to minimize some of them.

Common hypothesis of mutual exclusivity

Recently, two biologically plausible hypotheses of mutual exclu-

sivity have been proposed (Figure 1A, Table 1). The first hypoth-

esis is that mutually exclusive genomic events are functionally

linked in a common biological pathway (Figure 1B)[15, 24–27].

These mutually exclusive genes are supposed to affect the same

downstream effectors, thus showing functional redundancy.

Therefore, it is assumed that they share selection bonus, that is

the aberration of one gene is enough to completely disrupt their

common pathways, eliminating the selective pressure for alter-

ation of the others [28]. For example, FGFR3 and RAS mutations

were mutually exclusive in bladder cancer, and simultaneous

alteration of them could not further increase proliferation

phenotype [29]. The functional redundancy hypothesis of mu-

tual exclusivity can be indicative for aberrant functional status

of cancer genome, and can help reveal functional roles un-

known genetic alterations [24, 30, 31].

The second hypothesis is that the co-occurrence of these mu-

tually exclusive genetic alterations is detrimental to cancer cell

survival [32, 33]; therefore, the cells with such co-occurrence are

eliminated from the cancer cell population (Figure 1C). Particularly,

for a pair of mutually exclusive genes, if genetic alteration of either

alone is compatible with cell survival but alteration of both will

result in cell death, these two genes are synthetic lethal [23].

For example, BRCA1 mutation and CCNE1 amplification were mu-

tually exclusive in ovarian cancer [33]. Suppression of BRCA1 in

CCNE1-amplified cells can result in synthetic lethality. A possible

explanation is that mutation of BRCA1 destroys intact homolo-

gous recombination function that is necessary for the survival of

CCNE1-amplified cells, which already show genomic instability.

Notably, synthetic lethality can improve effectiveness of target

therapy in a context-dependent manner, which can provide new

insights into therapeutic design [34]. For example, inactivation of

PARP1/2 was mutually exclusive with BRCA1/2 mutation [35].

Adding the PARP1/2 inhibitor to BRCA1 or BRCA2 deficient cells

led to favorable responses in several clinical trials and retrospect-

ive analyses [36]. Similar cases include KRAS and EGFR in lung

adenocarcinomas [22], and WT1 and TET2 in acute myeloid leuke-

mia [37].

Computational methods for identifying

mutual exclusivity

At present, numerous computational methods have been de-

veloped to identify mutual exclusivity (Table 2). These methods

can be broadly divided into two categories: de novo methods and

knowledge-based methods (Figure 2). The de novo methods only

use genomic data to explore nonrandom mutually exclusive

patterns, while the knowledge-based methods integrate other

information, such as signaling pathways, protein–protein inter-

action networks and/or phenotype data. Below, we discuss sev-

eral representative computational methods, and comment on

their advantages and disadvantages.

De novo identification of mu tual exclusivity

Pairwise test for mutual exclusivity

The simplest way to identify mutual exclusivity is to perform

pairwise statistical analysis. As an initial study, Yeang et al. [38]

used Fisher’s exact test and likelihood ratio methods (Figure 2Aa)

to explore combinatorial patterns of somatic mutations and

found tissue-specific mutually exclusive patterns. However, both

of these two methods assumed that genetic alterations are

evenly distributed across samples, which has proved to be prob-

lematic [12, 31, 39, 40]. To address this problem, Kim et al.[41]

developed WeSME. WeSME estimates the null distribution of the

mutation profile by weighted sampling, which was proportional

to the observed mutation frequency. By comparing the number

of patients covered by a pair of exclusive genes with those from

the null distribution, the significance of the mutual exclusivity is

determined.

Combinatorial score for mutually exclusive modules

Indeed, mutual exclusivity may involve more than a pair of

genes, forming mutually exclusive modules [42]. The pairwise

test methods are not suitable for identifying mutually exclusive

modules because they may be too strict to find some biologically

significant mutually exclusive pairs [43]. Instead, combinatorial

score methods are developed to identify mutually exclusive gene

modules (Figure 2Ab) [44–46]. In principle, in a mutually exclu-

sive module, the alterations of member genes should cover more

samples (called high coverage), most samples should be covered

by exactly one module member (called high exclusivity), and few

2|Deng et al.

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