ArticleBioinformatics (Oxford, England)2013
Identification of cancer fusion drivers using network fusion centrality.
Article in Bioinformatics (Oxford, England), 2013. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed, 23 citations in OpenAlex.
- Oncogenic gene fusions in cancer: from biology to therapy.Signal transduction and targeted therapy · 2025Review
- A functional genomic approach to actionable gene fusions for precision oncology.Science advances · 2022Article
- Prediction of biomarkers and therapeutic combinations for anti-PD-1 immunotherapy using the global gene network association.Nature communications · 2022Article
- Identification of the key target profiles underlying the drugs of narrow therapeutic index for treating cancer and cardiovascular disease.Computational and structural biotechnology journal · 2021Article
- Identification of functionally connected multi-omic biomarkers for Alzheimer's disease using modularity-constrained Lasso.PloS one · 2020Article
- Molecular Signatures of Fusion Proteins in Cancer.ACS pharmacology & translational science · 2019Article
- Synstable Fusion: A Network-Based Algorithm for Estimating Driver Genes in Fusion Structures.Molecules (Basel, Switzerland) · 2018Article
- FusionPathway: Prediction of pathways and therapeutic targets associated with gene fusions in cancer.PLoS computational biology · 2018Article
- Kinase impact assessment in the landscape of fusion genes that retain kinase domains: a pan-cancer study.Briefings in bioinformatics · 2018Article
- TumorFusions: an integrative resource for cancer-associated transcript fusions.Nucleic acids research · 2018Article
- ChiPPI: a novel method for mapping chimeric protein-protein interactions uncovers selection principles of protein fusion events in cancer.Nucleic acids research · 2017Article
- RNA-seq Based Transcription Characterization of Fusion Breakpoints as a Potential Estimator for Its Oncogenic Potential.BioMed research international · 2017Article
- Bridging the gap between clinicians and systems biologists: from network biology to translational biomedical research.Journal of translational medicine · 2016Review
- RWCFusion: identifying phenotype-specific cancer driver gene fusions based on fusion pair random walk scoring method.Oncotarget · 2016Article
- Discovering and understanding oncogenic gene fusions through data intensive computational approaches.Nucleic acids research · 2016Review
- Gene expression in rat models for inter-generational transmission of islet dysfunction and obesity.Genomics data · 2014Article
- Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer.BMC systems biology · 2014Article
Corrections and comments
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Authors and funding
5 authors at 2 institutions in 1 country.
Funding
Abstract
summaryGene fusions are being discovered at an increasing rate using massively parallel sequencing technologies. Prioritization of cancer fusion drivers for validation cannot be performed using traditional single-gene based methods because fusions involve portions of two partner genes. To address this problem, we propose a novel network analysis method called fusion centrality that is specifically tailored for prioritizing gene fusions. We first propose a domain-based fusion model built on the theory of exon/domain shuffling. The model leads to a hypothesis that a fusion is more likely to be an oncogenic driver if its partner genes act like hubs in a network because the fusion mutation can deregulate normal functions of many other genes and their pathways. The hypothesis is supported by the observation that for most known cancer fusion genes, at least one of the fusion partners appears to be a hub in a network, and even for many fusions both partners appear to be hubs. Based on this model, we construct fusion centrality, a multi-gene-based network metric, and use it to score fusion drivers. We show that the fusion centrality outperforms other single gene-based methods. Specifically, the method successfully predicts most of 38 newly discovered fusions that had validated oncogenic importance. To our best knowledge, this is the first network-based approach for identifying fusion drivers. AVAILABILITY: Matlab code implementing the fusion centrality method is available upon request from the corresponding authors.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.