Telomere maintenance predicts progression and survival in pediatric intracranial ependymoma
Bibliographic record
Abstract
10020 Background: Ependymoma is the third most common pediatric brain tumor. Despite the importance of surgical resection and pathology, its clinical course is highly unpredictable and the biological behavior is largely unknown. As part of ongoing studies to identify potential biological and therapeutic markers for the disease, we analyzed the role of telomere maintenance, which is required for cancers to sustain growth and evade senescence. Methods: We analyzed 111 primary and recurrent ependymomas that were resected at the Hospital for Sick Children in Toronto between 1986 and 2004. A tissue array was constructed. hTERT and γH2AX expression were evaluated. Thirty-one frozen samples were additionally analyzed for telomere length (TRF) and telomerase activity (TRAP assay). Results: Of the 111 samples, 40 tumors were hTERT(-) with progression-free survival (PFS) of 83±15% and 71 were hTERT(+) with PFS of 21±9% (p<0.0001). Of the 65 patients with primary ependymomas, five-yr overall survival (OS) were 84±7% and 41±7% for hTERT(-) and hTERT(+) tumors, respectively (p=0.001). There was good correlation between telomerase activity and hTERT expression (κ=0.637). Multivariate analysis revealed hTERT expression to be the single most important predictor of survival of all known pathological, clinical and treatment factors (HR 60.4 (CI 6.4–561)). TRF measurement revealed lack of alternative lengthening of telomeres (ALT) and heterogenous telomere length in pediatric ependymomas. We observed a trend for better prognosis with telomere dysfunction manifested by shorter telomeres and positive γH2AX. Conclusions: This study suggest that telomere maintenance represents the first known biologic prognostic factor for intracranial ependymomas. Telomerase activity highlights the importance of senescence in carcinogenesis and a possible target for novel treatment approaches for the disease. No significant financial relationships to disclose.
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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 | 0.002 |
| 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) | 0.002 | 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".