Investigation of the In Vitro Therapeutic Efficacy of Nilotinib in Immortalized Human NF2-Null Vestibular Schwannoma Cells
Bibliographic record
Abstract
Vestibular schwannomas (VS) are a common posterior fossa brain tumor, and though benign can cause significant morbidity, particularly loss of hearing, tinnitus, vertigo and facial paralysis. The current treatment options for VS include microsurgical resection, stereotactic radiosurgery or close surveillance monitoring, with each treatment option carrying associated complications and morbidities. Most importantly, none of these options can definitively reverse hearing loss or tinnitus. Identification of a novel medical therapy, through the use of targeted molecular inhibition, is therefore a highly desirable treatment strategy that may minimize complications arising from both tumor and treatment and more importantly be suitable for patients whose options are limited with respect to surgical or radiosurgical interventions. In this study we chose to examine the effect of Nilotinib on VS. Nilotinib (Tasigna®) is a second-generation receptor tyrosine kinase (RTK) inhibitor with a target profile similar to that of imatinib (Gleevec®), but increased potency, decreased toxicity and greater cellular and tissue penetration. Nilotinib targets not only the BCR-ABL oncoprotein, but also platelet-derived growth factor (PDGF) receptor signalling. In this preclinical study, the human NF2-null schwannoma cell line HEI-193 subjected to nilotinib inhibition demonstrated decreased viability, proliferation and anchorage-independent growth, and increased apoptosis. A daily dose of nilotinib for 5 days inhibited HEI-I93 proliferation at a clinically-relevant concentration in a dose-dependent manner (IC(50) 3-5 µmol/L) in PDGF-stimulated cells. These anti-tumorigenic effects of nilotinib were correlated to inhibited activation of PDGFR-α and PDGFR-β and major downstream signalling pathways. These experiments support a therapeutic potential for Nilotinib in VS.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".