Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability

Deng, Yulan; Luo, Shangyi; Deng, Chunyu; Luo, Tao; Yin, Wenkang; Zhang, Hongyi; Zhang, Yong; Zhang, Xinxin; Lan, Yujia; Ping, Yanyan; Xiao, Yun; Li, Xia

doi:10.1093/bib/bbx109

Cited by 44 publications

(46 citation statements)

References 83 publications

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“…The de novo methods rely only on genetic data to discover novel genetic interactions, as well as cancer-related functional modules (Miller et al, 2011;Vandin et al, 2011b;Leiserson et al, 2013;Liu et al, 2017). Due to the large solution space such methods usually apply a prefiltering based on alteration frequency to reduce the inherent computational complexity which may reduce sensitivity by overlooking modules involving rare alterations (Deng et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

Ahmed

Baali

Erten

et al. 2019

Preprint

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Motivation: Genomic analyses from large cancer cohorts have revealed the mutational heterogeneity problem which hinders the identification of driver genes based only on mutation profiles. One way to tackle this problem is to incorporate the fact that genes act together in functional modules. The connectivity knowledge present in existing protein-protein interaction networks together with mutation frequencies of genes and the mutual exclusivity of cancer mutations can be utilized to increase the accuracy of identifying cancer driver modules. Results: We present a novel edge-weighted random walk-based approach that incorporates connectivity information in the form of protein-protein interactions, mutual exclusivity, and coverage to identify cancer driver modules. MEXCOwalk outperforms several state-of-the-art computational methods on TCGA pancancer data in terms of recovering known cancer genes, providing modules that are capable of classifying normal and tumor samples, and that are enriched for mutations in specific cancer types. Furthermore, the risk scores determined with output modules can stratify patients into low-risk and high-risk groups in multiple cancer types. MEXCOwalk identifies modules containing both well-known cancer genes and putative cancer genes that are rarely mutated in the pan-cancer data. The data, the source code, and useful scripts are available at: https://github.com/abu-compbio/MEXCOwalk. Contact:

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Section: Introductionmentioning

confidence: 99%

MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

Ahmed

Baali

Erten

et al. 2019

Preprint

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“…Functionally related driver mutations in the genome, also known as driver modules or pathways, activate the mechanisms by which cancer occurs, triggering cancer, driving cancer progression and giving cancer cells a selective advantage. Some computational methods and mathematical models have been developed to detect driver gene sets, pathways and modules by using large-scale sequencing data (Hou et al, 2016;Zheng et al, 2016;Yang et al, 2017;Xi et al, 2018;Ahmed et al, 2019;Deng et al, 2019;Zhang and Wang, 2019a;Pelegrina et al, 2020). Existing research show that the members of cancer driver modules often exhibit specific mutation patterns in cancer samples, the most significant characteristic is mutual exclusivity (mutex) which means once one member mutates, the tumor will gain a significant selection advantage, while later mutations in other members will not give the tumor a selection advantage.…”

Section: Introductionmentioning

confidence: 99%

“…Recently, two types of methods for identifying driver modules or gene sets have been proposed: De novo and knowledgebased methods. The De novo methods usually exploit two characteristics from somatic mutation data: high coverage and mutex (Dees et al, 2012;Vandin et al, 2012;Zhao et al, 2012;Babaei et al, 2013;Leiserson et al, 2013;Paull et al, 2013;Jia et al, 2014;Deng et al, 2019;Zhang and Wang, 2019a,b;Dees et al, 2012;Vandin et al, 2012;Zhao et al, 2012;Babaei et al, 2013;Leiserson et al, 2013;Paull et al, 2013;Jia et al, 2014;Deng et al, 2019;Zhang and Wang, 2019a,b). High coverage means that the driver modules or driver pathway covers a large number of samples.…”

Section: Introductionmentioning

confidence: 99%

An Effective Graph Clustering Method to Identify Cancer Driver Modules

Zhang

Zeng

Wang

et al. 2020

Front. Bioeng. Biotechnol.

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Identifying the molecular modules that drive cancer progression can greatly deepen the understanding of cancer mechanisms and provide useful information for targeted therapies. Most methods currently addressing this issue primarily use mutual exclusivity without making full use of the extra layer of module property. In this paper, we propose MCLCluster to identity cancer driver modules, which use somatic mutation data, Cancer Cell Fraction (CCF) data, gene functional interaction network and protein-protein interaction (PPI) network to derive the module property on mutual exclusivity, connectivity in PPI network and functionally similarity of genes. We have taken three effective measures to ensure the effectiveness of our algorithm. First, we use CCF data to choose stronger signals and more confident mutations. Second, the weighted gene functional interaction network is used to quantify the gene functional similarity in PPI. The third, graph clustering method based on Markov is exploited to extract the candidate module. MCLCluster is tested in the two TCGA datasets (GBM and BRCA), and identifies several well-known oncogenes driver modules and some modules with functionally associated driver genes. Besides, we compare it with Multi-Dendrix, FSME Cluster and RME in simulated dataset with background noise and passenger rate, MCLCluster outperforming all of these methods.

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“…Genetic alterations affecting pairwise interactions, such as Mutually Exclusive (ME) mutations have been studied extensively. ME can indicate functional redundancy or Synthetic Lethality (SL) [16]. SL interactions between two genes is a condition where the loss of either gene is viable but the loss of both is lethal, and has been considered a foundation for development of targeted anticancer therapies [7,39].…”

Section: Introductionmentioning

confidence: 99%

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

Liany

Lin

Jeyasekharan

et al. 2020

Preprint

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Study of pairwise genetic interactions such as mutual exclusivity or synthetic lethality has led to the development of targeted anticancer therapies, and mining the network of such interactions is a common approach used to obtain deeper insights into the mechanism of cancer. A number of useful graph clustering-based tools exist to mine interaction networks. We develop a new network mining algorithm that can provide a pathway-centric view that existing tools cannot. Our approach is based on finding edge-disjoint bipartite subgraphs of highest weights in an input network of genes, where edge weights indicate the significance of the interaction and each set of nodes in every bipartite subgraph is constrained to belong to a single pathway. This problem is NP-hard and we develop an Integer Linear Program to solve this problem. We evaluate our algorithm on breast and stomach cancer data. Our algorithm mines dense between-pathway interactions that are known to play important roles in cancer and are therapeutically actionable. Our algorithm complements existing network mining tools and can be useful to study the mutational landscape of cancer and inform therapy development.

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Identifying mutual exclusivity across cancer genomes: computational approaches to discover genetic interaction and reveal tumor vulnerability

Cited by 44 publications

References 83 publications

MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

MEXCOWalk: Mutual Exclusion and Coverage Based Random Walk to Identify Cancer Modules

An Effective Graph Clustering Method to Identify Cancer Driver Modules

Mining Pathway Associations from Networks of Mutual Exclusivity Interactions

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