ArticleGenome research2017
HIT'nDRIVE: patient-specific multidriver gene prioritization for precision oncology.
Article in Genome research, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 29 papers.
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Who cites it
29 citing papers in PubMed.
- GRAFT: a graph-aware fusion transformer for cancer driver gene prediction.Briefings in bioinformatics · 2026Article
- 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
- Pan-cancer and multiomics: advanced strategies for diagnosis, prognosis, and therapy in the complex genetic and molecular universe of cancer.Clinical & translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico · 2025Review
- Evolutionary dependency of cancer mutations in gene pairs inferred by nonsynonymous-synonymous mutation ratios.Genome medicine · 2024Article
- TMEM2 induces epithelial-mesenchymal transition and promotes resistance to temozolomide in GBM cells.Heliyon · 2023Article
- Identification of protein-protein interaction bridges for multiple sclerosis.Bioinformatics (Oxford, England) · 2023Article
- Single-cell RNA binding protein regulatory network analyses reveal oncogenic HNRNPK-MYC signalling pathway in cancer.Communications biology · 2023Article
- Identifying cancer driver genes in individual tumours.Computational and structural biotechnology journal · 2023Review
- NetMix2: A Principled Network Propagation Algorithm for Identifying Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2022Article
- DGMP: Identifying Cancer Driver Genes by Jointing DGCN and MLP from Multi-omics Genomic Data.Genomics, proteomics & bioinformatics · 2022Article
- Identification of risk genes for Alzheimer's disease by gene embedding.Cell genomics · 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
- SYSMut: decoding the functional significance of rare somatic mutations in cancer.Briefings in bioinformatics · 2022Article
- Driver gene detection through Bayesian network integration of mutation and expression profiles.Bioinformatics (Oxford, England) · 2022Article
- Identification of driver genes based on gene mutational effects and network centrality.BMC bioinformatics · 2021Article
- Identification of combinations of somatic mutations that predict cancer survival and immunotherapy benefit.NAR cancer · 2021Article
- NetMix: A Network-Structured Mixture Model for Reduced-Bias Estimation of Altered Subnetworks.Journal of computational biology : a journal of computational molecular cell biology · 2021Article
- Identification of Breast Cancer Subtype-Specific Biomarkers by Integrating Copy Number Alterations and Gene Expression Profiles.Medicina (Kaunas, Lithuania) · 2021Article
- Patient-Specific Network for Personalized Breast Cancer Therapy with Multi-Omics Data.Entropy (Basel, Switzerland) · 2021Article
- Untangling a complex web: Computational analyses of tumor molecular profiles to decode driver mechanisms.Journal of genetics and genomics = Yi chuan xue bao · 2020Review
Corrections and comments
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Authors and funding
11 authors.
Funding
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Abstract
Prioritizing molecular alterations that act as drivers of cancer remains a crucial bottleneck in therapeutic development. Here we introduce HIT'nDRIVE, a computational method that integrates genomic and transcriptomic data to identify a set of patient-specific, sequence-altered genes, with sufficient collective influence over dysregulated transcripts. HIT'nDRIVE aims to solve the "random walk facility location" (RWFL) problem in a gene (or protein) interaction network, which differs from the standard facility location problem by its use of an alternative distance measure: "multihitting time," the expected length of the shortest random walk from any one of the set of sequence-altered genes to an expression-altered target gene. When applied to 2200 tumors from four major cancer types, HIT'nDRIVE revealed many potentially clinically actionable driver genes. We also demonstrated that it is possible to perform accurate phenotype prediction for tumor samples by only using HIT'nDRIVE-seeded driver gene modules from gene interaction networks. In addition, we identified a number of breast cancer subtype-specific driver modules that are associated with patients' survival outcome. Furthermore, HIT'nDRIVE, when applied to a large panel of pan-cancer cell lines, accurately predicted drug efficacy using the driver genes and their seeded gene modules. Overall, HIT'nDRIVE may help clinicians contextualize massive multiomics data in therapeutic decision making, enabling widespread implementation of precision oncology.
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