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

Erten, Cesim; Houdjedj, Aissa; Kazan, Hilal; Bahmed, Ahmed Amine Taleb

doi:10.1093/bioinformatics/btac329

Cited by 7 publications

(4 citation statements)

References 79 publications

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“…Then, we defined personalized drivers predicted by PDRWH as the top-n ranked genes, where n was assigned as twice the median of number of mutated genes in the general driver set across the population of patients [ 21 ]: 8 for breast cancer (BRCA), 10 for kidney clear cell carcinoma (KIRC), 12 for liver cancer (LIHC), 8 for glioblastoma (GBM), and 16 for stomach cancer (STAD). We used the modified REA strategy proposed by PersonaDrive [ 21 ] for a comparison of PDRWH with six personalized prediction methods (DawnRank, Prodigy, SCS, PersonaDrive, Degree and Frequency) across five cancer types from TCGA. For each sample, the identified cancer drivers in the general driver list were adopted to compute the Precision, Recall, and F1-score.…”

Section: Methodsmentioning

confidence: 99%

“…More importantly, they are overly dependent on the data quality of individual samples, with poor tolerance for noise and low reliability of the results. To address this issue, PersonaDrive aims to utilize the comprehensive whole cohort data for guiding the personalized driver prediction [ 21 ]. This is achieved by constructing a bipartite graph to model pair-wise relationships among the set of mutated genes and the differently expressed genes.…”

Section: Introductionmentioning

confidence: 99%

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A novel hypergraph model for identifying and prioritizing personalized drivers in cancer

Zhang,

Ma,

Guo

et al. 2024

PLoS Comput Biol

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Cancer development is driven by an accumulation of a small number of driver genetic mutations that confer the selective growth advantage to the cell, while most passenger mutations do not contribute to tumor progression. The identification of these driver genes responsible for tumorigenesis is a crucial step in designing effective cancer treatments. Although many computational methods have been developed with this purpose, the majority of existing methods solely provided a single driver gene list for the entire cohort of patients, ignoring the high heterogeneity of driver events across patients. It remains challenging to identify the personalized driver genes. Here, we propose a novel method (PDRWH), which aims to prioritize the mutated genes of a single patient based on their impact on the abnormal expression of downstream genes across a group of patients who share the co-mutation genes and similar gene expression profiles. The wide experimental results on 16 cancer datasets from TCGA showed that PDRWH excels in identifying known general driver genes and tumor-specific drivers. In the comparative testing across five cancer types, PDRWH outperformed existing individual-level methods as well as cohort-level methods. Our results also demonstrated that PDRWH could identify both common and rare drivers. The personalized driver profiles could improve tumor stratification, providing new insights into understanding tumor heterogeneity and taking a further step toward personalized treatment. We also validated one of our predicted novel personalized driver genes on tumor cell proliferation by vitro cell-based assays, the promoting effect of the high expression of Low-density lipoprotein receptor-related protein 1 (LRP1) on tumor cell proliferation.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer

Zhang,

Ma,

Guo

et al. 2024

PLoS Comput Biol

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“…Although unique in its use of sCCA, our approach is not the only method that aims to identify disease related gene sets. Several other methods have been devised to for this purpose including, but not limited to, DriverNet [59], DawnRank [60], Prodigy [61], PersonaDrive [62], especially for different oncological applications. However, our approach has several important distinctions from these methods.…”

Section: Plos Onementioning

confidence: 99%

Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations

2022

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Background Copy number aberrations (CNAs) in cancer affect disease outcomes by regulating molecular phenotypes, such as gene expressions, that drive important biological processes. To gain comprehensive insights into molecular biomarkers for cancer, it is critical to identify key groups of CNAs, the associated gene modules, regulatory modules, and their downstream effect on outcomes. Methods In this paper, we demonstrate an innovative use of sparse canonical correlation analysis (sCCA) to effectively identify the ensemble of CNAs, and gene modules in the context of binary and censored disease endpoints. Our approach detects potentially orthogonal gene expression modules which are highly correlated with sets of CNA and then identifies the genes within these modules that are associated with the outcome. Results Analyzing clinical and genomic data on 1,904 breast cancer patients from the METABRIC study, we found 14 gene modules to be regulated by groups of proximally located CNA sites. We validated this finding using an independent set of 1,077 breast invasive carcinoma samples from The Cancer Genome Atlas (TCGA). Our analysis of 7 clinical endpoints identified several novel and interpretable regulatory associations, highlighting the role of CNAs in key biological pathways and processes for breast cancer. Genes significantly associated with the outcomes were enriched for early estrogen response pathway, DNA repair pathways as well as targets of transcription factors such as E2F4, MYC, and ETS1 that have recognized roles in tumor characteristics and survival. Subsequent meta-analysis across the endpoints further identified several genes through the aggregation of weaker associations. Conclusions Our findings suggest that sCCA analysis can aggregate weaker associations to identify interpretable and important genes, modules, and clinically consequential pathways.

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“…Further, PRODIGY [ 20 ] adopts Steiner tree model to evaluate the impact of the genes with mutations on the deregulated pathways to identify personalized cancer driver genes. Later, PersonaDrive [ 21 ] tries to construct a personalized bipartite graph that links mutated genes to differentially expressed genes for each patient, and calculates the edge weights of the graph based on the overlap between the mutated gene and the differentially expressed gene pair in biological pathways. Subsequently, it ranks the the potential driver genes based on their influence scores evaluated by the edge weights in the bipartite graph.…”

Section: Introductionmentioning

confidence: 99%

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

Huang,

Chen,

Sun

et al. 2024

BMC Bioinformatics

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Background Driver genes play a vital role in the development of cancer. Identifying driver genes is critical for diagnosing and understanding cancer. However, challenges remain in identifying personalized driver genes due to tumor heterogeneity of cancer. Although many computational methods have been developed to solve this problem, few efforts have been undertaken to explore gene-patient associations to identify personalized driver genes. Results Here we propose a method called LPDriver to identify personalized cancer driver genes by employing linear neighborhood propagation model on individual genetic data. LPDriver builds personalized gene network based on the genetic data of individual patients, extracts the gene-patient associations from the bipartite graph of the personalized gene network and utilizes a linear neighborhood propagation model to mine gene-patient associations to detect personalized driver genes. The experimental results demonstrate that as compared to the existing methods, our method shows competitive performance and can predict cancer driver genes in a more accurate way. Furthermore, these results also show that besides revealing novel driver genes that have been reported to be related with cancer, LPDriver is also able to identify personalized cancer driver genes for individual patients by their network characteristics even if the mutation data of genes are hidden. Conclusions LPDriver can provide an effective approach to predict personalized cancer driver genes, which could promote the diagnosis and treatment of cancer. The source code and data are freely available at https://github.com/hyr0771/LPDriver.

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

Cited by 7 publications

References 79 publications

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer

A novel hypergraph model for identifying and prioritizing personalized drivers in cancer

Sparse canonical correlation to identify breast cancer related genes regulated by copy number aberrations

Exploring gene-patient association to identify personalized cancer driver genes by linear neighborhood propagation

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