ArticleNucleic acids research2015
Patient-specific driver gene prediction and risk assessment through integrated network analysis of cancer omics profiles.
Article in Nucleic acids research, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 58 papers.
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Who cites it
58 citing papers in PubMed.
- Functional germline variants together with somatic mutations alter the integrity of cancer hallmark regulatory networks.Genome medicine · 2026Article
- Emerging pathological mechanisms of Alzheimer's disease pathogenesis: from neuroimmune interactions to intercellular communication.Frontiers in aging neuroscience · 2026Review
- A new algorithm Precision OncoPanels (PrOPs) identifies short individualized actionable panels that can guide cancer treatment: a pan-cancer analysis of TCGA cohorts.NAR genomics and bioinformatics · 2025Article
- TARGET-SL: precision essential gene prediction using driver prioritisation and synthetic lethality.Briefings in bioinformatics · 2025Article
- Integrative Computational Framework,bioRxiv : the preprint server for biology · 2024Article
- Integrative genomic analyses of European intrahepatic cholangiocarcinoma: Novel ROS1 fusion gene and PBX1 as prognostic marker.Clinical and translational medicine · 2024Article
- Personalized Driver Gene Prediction Using Graph Convolutional Networks with Conditional Random Fields.Biology · 2024Article
- Gsw-fi: a GLM model incorporating shrinkage and double-weighted strategies for identifying cancer driver genes with functional impact.BMC bioinformatics · 2024Article
- Prediction of cancer driver genes and mutations: the potential of integrative computational frameworks.Briefings in bioinformatics · 2024Review
- Dual network analysis of transcriptome data for discovery of new therapeutic targets in non-small cell lung cancer.Oncogene · 2023Article
- Network embedding framework for driver gene discovery by combining functional and structural information.BMC genomics · 2023Article
- Molecular subtyping in colorectal cancer: A bridge to personalized therapy (Review).Oncology letters · 2023Review
- Identification of Cancer Driver Genes by Integrating Multiomics Data with Graph Neural Networks.Metabolites · 2023Article
- Model performance and interpretability of semi-supervised generative adversarial networks to predict oncogenic variants with unlabeled data.BMC bioinformatics · 2023Review
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- A novel candidate disease gene prioritization method using deep graph convolutional networks and semi-supervised learning.BMC bioinformatics · 2022Article
- Prioritization of cancer driver gene with prize-collecting steiner tree by introducing an edge weighted strategy in the personalized gene interaction network.BMC bioinformatics · 2022Article
- Identifying network biomarkers of cancer by sample-specific differential network.BMC bioinformatics · 2022Article
- Article
- Driver gene detection through Bayesian network integration of mutation and expression profiles.Bioinformatics (Oxford, England) · 2022Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
Extensive and multi-dimensional data sets generated from recent cancer omics profiling projects have presented new challenges and opportunities for unraveling the complexity of cancer genome landscapes. In particular, distinguishing the unique complement of genes that drive tumorigenesis in each patient from a sea of passenger mutations is necessary for translating the full benefit of cancer genome sequencing into the clinic. We address this need by presenting a data integration framework (OncoIMPACT) to nominate patient-specific driver genes based on their phenotypic impact. Extensive in silico and in vitro validation helped establish OncoIMPACT's robustness, improved precision over competing approaches and verifiable patient and cell line specific predictions (2/2 and 6/7 true positives and negatives, respectively). In particular, we computationally predicted and experimentally validated the gene TRIM24 as a putative novel amplified driver in a melanoma patient. Applying OncoIMPACT to more than 1000 tumor samples, we generated patient-specific driver gene lists in five different cancer types to identify modes of synergistic action. We also provide the first demonstration that computationally derived driver mutation signatures can be overall superior to single gene and gene expression based signatures in enabling patient stratification and prognostication. Source code and executables for OncoIMPACT are freely available from http://sourceforge.net/projects/oncoimpact.
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Registered trials
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.