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

Ahmed, Rafsan; Baali, Ilyes; Erten, Cesim; Hoxha, Evis; Kazan, Hilal

doi:10.1101/547653

Cited by 3 publications

(7 citation statements)

References 42 publications

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“…Since a driver pathway tends to be perturbed in a relatively large number of patients, one of the desired properties of each module is large coverage 17 , 25 , 26 . We define the coverage of M as, .…”

Section: Methodsmentioning

confidence: 99%

“…We define the coverage of M as, . Several cancer driver module identification methods have additionally made use of the concept of mutual exclusivity 20 , 24 – 26 , 33 . It refers to the phenomenon that for a group of genes which exhibit evidence of shared functional pathway, simultaneous mutations in the same patients are less frequent than is expected by chance 18 .…”

Section: Methodsmentioning

confidence: 99%

“…The ODMIC problem definition is in part inspired by the cancer driver module identification problem definition of MEXCOWalk 26 . One crucial difference is that the MEXCOWalk definition does not allow overlaps.…”

Section: Methodsmentioning

confidence: 99%

“…Prioritizing cancer genes based on a combined score of coverage and mutual exclusivity has been employed in several previous approaches 21 , 25 , 26 . In line with our ODMIC problem definition, we similarly make use of coverage and mutual exclusivity values in the form of our module score definition.…”

Section: Methodsmentioning

confidence: 99%

“…MEMCover is also based on a greedy iterative seed-and-extend heuristic where a function that integrates coverage, mutual exclusivity and confidence values of interactions in the network is maximized 25 . MEXCOWalk extends Hotnet2’s random walk strategy by introducing edge weights that include mutual exclusity and coverage 26 .…”

Section: Introductionmentioning

confidence: 99%

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DriveWays: a method for identifying possibly overlapping driver pathways in cancer

Baali

Erten

Kazan

2020

Sci Rep

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The majority of the previous methods for identifying cancer driver modules output nonoverlapping modules. This assumption is biologically inaccurate as genes can participate in multiple molecular pathways. This is particularly true for cancer-associated genes as many of them are network hubs connecting functionally distinct set of genes. It is important to provide combinatorial optimization problem definitions modeling this biological phenomenon and to suggest efficient algorithms for its solution. We provide a formal definition of the Overlapping Driver Module Identification in Cancer (ODMIC) problem. We show that the problem is NP-hard. We propose a seed-and-extend based heuristic named DriveWays that identifies overlapping cancer driver modules from the graph built from the IntAct PPI network. DriveWays incorporates mutual exclusivity, coverage, and the network connectivity information of the genes. We show that DriveWays outperforms the state-of-the-art methods in recovering well-known cancer driver genes performed on TCGA pan-cancer data. Additionally, DriveWay’s output modules show a stronger enrichment for the reference pathways in almost all cases. Overall, we show that enabling modules to overlap improves the recovery of functional pathways filtered with known cancer drivers, which essentially constitute the reference set of cancer-related pathways.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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DriveWays: a method for identifying possibly overlapping driver pathways in cancer

Baali

Erten

Kazan

2020

Sci Rep

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Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

Chen

et al. 2021

Interdiscip Sci Comput Life Sci

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Background: With the constant update of large-scale sequencing data and the continuous improvement of cancer genomics data such as the cancer genome atlas ICGC and TCGA, it gains increasing importance how to detect the functional high-frequency mutation gene set in cells that causes cancer within the field of medicine.Methods: In this study, to solve the issue of mutated gene heterogeneity and improve the accuracy of driver modules, we propose a new recognition method of driver modules, named ECSWalk, based on the human protein interaction networks and pan-cancer somatic mutation data. This study firstly utilizes high mutual exclusivity and high coverage between mutation genes and topological structure similarity of the nodes in complex networks to calculate interaction weights between genes. Secondly, the method of random walk with restart is utilized to construct a weighted directed network, and the strong connectivity principle of the directed graph is utilized to create the initial candidate modules with a certain number of genes. Finally, the large modules in the candidate modules are reasonably split using the way of the induced subgraph, and the small modules are expanded using a greedy strategy to obtain the optimal driver modules.Results: This method is applied to the analysis of TCGA pan-cancer data, and the experimental results show that ECSWalk can detect driver modules more effectively and accurately, and can identify new candidate gene sets with higher biological relevance and statistical significance than MEXCOWalk and HotNet2.Conclusions: ECSWalk is of theoretical guidance and practical value for cancer diagnosis, treatment and drug targets.

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Ranking cancer drivers via betweenness-based outlier detection and random walks

2021

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Background Recent cancer genomic studies have generated detailed molecular data on a large number of cancer patients. A key remaining problem in cancer genomics is the identification of driver genes. Results We propose BetweenNet, a computational approach that integrates genomic data with a protein-protein interaction network to identify cancer driver genes. BetweenNet utilizes a measure based on betweenness centrality on patient specific networks to identify the so-called outlier genes that correspond to dysregulated genes for each patient. Setting up the relationship between the mutated genes and the outliers through a bipartite graph, it employs a random-walk process on the graph, which provides the final prioritization of the mutated genes. We compare BetweenNet against state-of-the art cancer gene prioritization methods on lung, breast, and pan-cancer datasets. Conclusions Our evaluations show that BetweenNet is better at recovering known cancer genes based on multiple reference databases. Additionally, we show that the GO terms and the reference pathways enriched in BetweenNet ranked genes and those that are enriched in known cancer genes overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods.

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

Cited by 3 publications

References 42 publications

DriveWays: a method for identifying possibly overlapping driver pathways in cancer

DriveWays: a method for identifying possibly overlapping driver pathways in cancer

Integrating Protein–Protein Interaction Networks and Somatic Mutation Data to Detect Driver Modules in Pan-Cancer

Ranking cancer drivers via betweenness-based outlier detection and random walks

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