ArticleScientific reports2016
Network Modeling Identifies Patient-specific Pathways in Glioblastoma.
Article in Scientific reports, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
18 citing papers in PubMed, 32 citations in OpenAlex.
- Bioprinting of GelMA-Based Hydrogels to Aid in Creation of Biomimetic 3D Models for Glioblastoma.Micromachines · 2025Article
- Molecular principles underlying aggressive cancers.Signal transduction and targeted therapy · 2025Review
- Integration of transcriptomics, proteomics and loss-of-function screening reveals WEE1 as a target for combination with dasatinib against proneural glioblastoma.Cancer letters · 2024Article
- Delineating the glioblastoma stemness by genes involved in cytoskeletal rearrangements and metabolic alterations.World journal of stem cells · 2023Review
- Integrating mechanism-based modeling with biomedical imaging to build practical digital twins for clinical oncology.Biophysics reviews · 2022Review
- Context dependent isoform specific PI3K inhibition confers drug resistance in hepatocellular carcinoma cells.BMC cancer · 2022Article
- Phosphoproteomics: a valuable tool for uncovering molecular signaling in cancer cells.Expert review of proteomics · 2021Review
- Article
- ORN: Inferring patient-specific dysregulation status of pathway modules in cancer with OR-gate Network.PLoS computational biology · 2021Article
- Transcriptome profiles associated with selenium-deficiency-dependent oxidative stress identify potential diagnostic and therapeutic targets in liver cancer cells.Turkish journal of biology = Turk biyoloji dergisi · 2021Article
- Performance Assessment of the Network Reconstruction Approaches on Various Interactomes.Frontiers in molecular biosciences · 2021Article
- Transcriptomics predicts compound synergy in drug and natural product treated glioblastoma cells.PloS one · 2020Article
- 3D spatial organization and network-guided comparison of mutation profiles in Glioblastoma reveals similarities across patients.PLoS computational biology · 2019Article
- The Emerging Potential for Network Analysis to Inform Precision Cancer Medicine.Journal of molecular biology · 2018Review
- FunGeneNet: a web tool to estimate enrichment of functional interactions in experimental gene sets.BMC genomics · 2018Article
- Glioblastoma: new therapeutic strategies to address cellular and genomic complexity.Oncotarget · 2018Review
- Integrating Transcriptomic Data with Mechanistic Systems Pharmacology Models for Virtual Drug Combination Trials.ACS chemical neuroscience · 2018Article
- Identification of oral cancer related candidate genes by integrating protein-protein interactions, gene ontology, pathway analysis and immunohistochemistry.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
Abstract
Glioblastoma is the most aggressive type of malignant human brain tumor. Molecular profiling experiments have revealed that these tumors are extremely heterogeneous. This heterogeneity is one of the principal challenges for developing targeted therapies. We hypothesize that despite the diverse molecular profiles, it might still be possible to identify common signaling changes that could be targeted in some or all tumors. Using a network modeling approach, we reconstruct the altered signaling pathways from tumor-specific phosphoproteomic data and known protein-protein interactions. We then develop a network-based strategy for identifying tumor specific proteins and pathways that were predicted by the models but not directly observed in the experiments. Among these hidden targets, we show that the ERK activator kinase1 (MEK1) displays increased phosphorylation in all tumors. By contrast, protein numb homolog (NUMB) is present only in the subset of the tumors that are the most invasive. Additionally, increased S100A4 is associated with only one of the tumors. Overall, our results demonstrate that despite the heterogeneity of the proteomic data, network models can identify common or tumor specific pathway-level changes. These results represent an important proof of principle that can improve the target selection process for tumor specific treatments.
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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.