Telomerase Inhibition as a Novel Therapy for Pediatric Ependymoma
Bibliographic record
Abstract
Ependymomas are the third most common pediatric brain tumor with an overall survival of approximately 50%. Recently, we showed that telomerase [human telomerase reverse transcriptase (hTERT)] expression is a predictor of poor outcome in pediatric ependymoma. Thus, we hypothesized that ependymomas with functional telomerase may behave more aggressively and that these patients may benefit from anti-telomerase therapy. To address our hypothesis, we investigated the effect of telomerase inhibition on primary ependymoma cells harvested at the time of surgery, as no animal models or established cell lines are readily available for this tumor. The cells were characterized for glial fibrillary acidic protein (GFAP) and hTERT expression, initial telomere length and telomerase activity. They were then subjected to telomerase inhibition (MST-312, 1 microM) and tested for effects on cell viability (MTT assay), proliferation (MIB-1), apoptosis (cleaved caspase 3) and DNA damage (gammaH2AX). After 72 h of telomerase inhibition, primary ependymoma cells showed a significant decrease in cell number (P < 0.001), accompanied by increased DNA damage (gammaH2AX expression) (P < 0.01) and decreased proliferative index (MIB-1) (P < 0.01). Half showed an increase in apoptosis (cleaved caspase 3). These data suggest that telomerase inhibition may be an effective adjuvant therapy in pediatric ependymoma, potentially inducing tumor growth arrest in the short term, independent of telomere shortening.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".