ArticleJournal of neuro-oncology2024
Disrupting glioblastoma networks with tumor treating fields (TTFields) in in vitro models.
Article in Journal of neuro-oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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Who cites it
5 citing papers in PubMed.
- Harnessing induced pluripotent stem cells and organoids for disease modeling and precision medicine.Stem cell research & therapy · 2026Review
- Review
- Overcoming temozolomide resistance in glioma: recent advances and mechanistic insights.Acta neuropathologica communications · 2025Review
- Review
- The tumorigenic effect of the high expression of ABRACL in glioma and its potential as a therapeutic target.Heliyon · 2024Article
Corrections and comments
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Authors and funding
11 authors.
Funding
Abstract
purposeThis study investigates the biological effect of Tumor Treating Fields (TTFields) on key drivers of glioblastoma's malignancy-tumor microtube (TM) formation-and on the function and overall integrity of the tumor cell network.
methodUsing a two-dimensional monoculture GB cell network model (2DTM) of primary glioblastoma cell (GBC) cultures (S24, BG5 or T269), we evaluated the effects of TTFields on cell density, interconnectivity and structural integrity of the tumor network. We also analyzed calcium (Ca
resultsIn the 2DTM assay, TTFields reduced cell density by 85-88% and disrupted network interconnectivity, particularly in cells with multiple TMs. A "crooked TM" phenotype emerged in 5-6% of treated cells, rarely seen in controls. Ca
conclusionOur comprehensive study provides new insights into the multiple effects of Inovitro-modeled TTFields on glioma progression, morphology and network dynamics in vitro. Future in vivo studies to verify our in vitro findings may provide the basis for a deeper understanding and optimization of TTFields as a therapeutic modality in the treatment of GB.
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