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

Author:

Erten Cesim1,Houdjedj Aissa12,Kazan Hilal1ORCID,Taleb Bahmed Ahmed Amine3

Affiliation:

1. Department of Computer Engineering, Antalya Bilim University , Antalya 07190, Turkey

2. Department of Computer Engineering, Akdeniz University , Antalya 07070, Turkey

3. Institute of Postgraduate Education, Electrical and Computer Engineering Graduate Program, Antalya Bilim University , Antalya 07190, Turkey

Abstract

Abstract 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 five 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. In addition, 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 toward the development of personalized treatments and therapies. Availability and implementation All the codes and data are available at https://github.com/abu-compbio/PersonaDrive, and the data underlying this article are available in Zenodo, at https://doi.org/10.5281/zenodo.6520187. Supplementary information Supplementary data are available at Bioinformatics online.

Funder

Scientific and Technological Research Council of Turkey

Health Institutes of Turkey

Publisher

Oxford University Press (OUP)

Subject

Computational Mathematics,Computational Theory and Mathematics,Computer Science Applications,Molecular Biology,Biochemistry,Statistics and Probability

Reference54 articles.

1. MEXCOwalk: mutual exclusion and coverage based random walk to identify cancer modules;Ahmed;Bioinformatics,2020

2. DriveWays: a method for identifying possibly overlapping driver pathways in cancer;Baali;Sci. Rep,2020

3. The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity;Barretina;Nature,2012

4. DriverNet: uncovering the impact of somatic driver mutations on transcriptional networks in cancer;Bashashati;Genome Biol,2012

5. Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles;Bertrand;Nucleic Acids Res,2015

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