Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

Erten, Cesim; Houdjedj, Aissa; Kazan, Hilal

doi:10.1101/2020.03.03.974295

Cited by 2 publications

(1 citation statement)

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“…The bipartite graph model employed by DriverNet to relate the set of mutated genes to the set of DEGs has inspired many subsequent driver identification methods. BetweenNet is one such recent method which utilizes a measure based on betweenness centralities of genes in proteinprotein interaction (PPI) networks constructed for each patient to identify dysregulated genes and employs a random-walk process on the network to prioritize driver genes (Erten et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

PersonaDrive: a method for the identification and prioritization of personalized cancer drivers

et al. 2022

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Motivation A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. Results We propose a novel method that integrates genomic data, biological pathways, and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted ‘pairwise pathway coverage’ scores across all the samples, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against three state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell line data. Additionally, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines’ reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information towards the development of personalized treatments and therapies. Availability All the code and data are available at https://github.com/abu-compbio/PersonaDrive (archived at https://doi.org/10.5281/zenodo.6520187). Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Introductionmentioning

confidence: 99%

PersonaDrive: a method for the identification and prioritization of personalized cancer drivers

et al. 2022

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PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers

Erten

Houdjedj

Kazan

et al. 2021

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Motivation: A major challenge in cancer genomics is to distinguish the driver mutations that are causally linked to cancer from passenger mutations that do not contribute to cancer development. The majority of existing methods provide a single driver gene list for the entire cohort of patients. However, since mutation profiles of patients from the same cancer type show a high degree of heterogeneity, a more ideal approach is to identify patient-specific drivers. Results: We propose a novel method that integrates genomic data, biological pathways, and protein connectivity information for personalized identification of driver genes. The method is formulated on a personalized bipartite graph for each patient. Our approach provides a personalized ranking of the mutated genes of a patient based on the sum of weighted 'pairwise pathway coverage' scores across all the patients, where appropriate pairwise patient similarity scores are used as weights to normalize these coverage scores. We compare our method against three state-of-the-art patient-specific cancer gene prioritization methods. The comparisons are with respect to a novel evaluation method that takes into account the personalized nature of the problem. We show that our approach outperforms the existing alternatives for both the TCGA and the cell-line data. Additionally, we show that the KEGG/Reactome pathways enriched in our ranked genes and those that are enriched in cell lines' reference sets overlap significantly when compared to the overlaps achieved by the rankings of the alternative methods. Our findings can provide valuable information towards the development of personalized treatments and therapies.

show abstract

Ranking Cancer Drivers via Betweenness-based Outlier Detection and Random Walks

Cited by 2 publications

References 69 publications

PersonaDrive: a method for the identification and prioritization of personalized cancer drivers

PersonaDrive: a method for the identification and prioritization of personalized cancer drivers

PersonaDrive: A Method for the Identification and Prioritization of Personalized Cancer Drivers

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