Human Telomere Reverse Transcriptase Expression Predicts Progression and Survival in Pediatric Intracranial Ependymoma
Bibliographic record
Abstract
PURPOSE: Pediatric intracranial ependymomas are a heterogeneous group of neoplasms with unpredictable clinical and biologic behavior. As part of ongoing studies to identify potential biologic and therapeutic markers, we analyzed the role of human telomere reverse transcriptase (hTERT; the catalytic subunit of telomerase) expression as a prognostic marker for this disease. PATIENTS AND METHODS: Primary intracranial ependymomas that were resected at our institution between 1986 and 2004 were identified through the pathology and oncology databases. A tissue array was constructed from the patient samples and hTERT expression was evaluated by immunohistochemistry. Twenty-one samples were also analyzed for telomerase activity (telomerase repeat amplification protocol assay). RESULTS: Eighty-seven tumors from 65 patients were analyzed. Five-year progression-free survival was 57% (SEM, 12%) and 21% (SEM, 8%) for hTERT-negative and hTERT-positive tumors, respectively (P = .002). Five-year overall survival was 84% (SEM, 7%) and 41% (SEM, 7%) for hTERT-negative and hTERT-positive tumors, respectively (P = .001). There was good correlation between telomerase activity and hTERT expression (kappa = 0.637). Multivariate analysis revealed hTERT expression to be the single most important predictor of survival of all known pathologic, clinical, and treatment factors (hazard ratio, 60.4; 95% CI, 6.4 to 561). All four patients with hTERT-negative tumors at relapse are still alive, with median follow-up of 11.2 years. CONCLUSION: In this study, hTERT expression was the strongest predictor of outcome and was independent of other clinical and pathologic prognostic markers. It represents a simple and reliable biologic prognostic factor for intracranial ependymomas. These results should be confirmed in larger prospective trials.
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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 | 0.001 |
| 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".