MB-53hTERT EXPRESSION AND REGULATION IN PEDIATRIC MEDULLOBLASTOMA (MB)
Bibliographic record
Abstract
BACKGROUND: Telomerase reactivation, critical in many cancers, correlates with expression of its catalytic subunit hTERT. The prognostic significance of telomere maintenance mechanisms in pediatric MB has not been well described. METHODS: In this multi-institutional retrospective study of telomerase expression and hTERT regulation in newly-diagnosed children with MB, hTERT and c-MYC expression were assessed by qRT-PCR, normalized to non-neoplastic brain control samples. hTERT promoter methylation was analyzed using quantitative pyrosequencing and Illumina 450k methylation array. Cox proportional-hazard regression analyses evaluated the association of hTERT expression with progression-free survival (PFS) or overall survival (OS). Spearman and Kruskal-Wallis tests were used to correlate hTERT promoter methylation and expression and to assess variations among MB subgroups, respectively. RESULTS: Among 74 patients with hTERT expression and outcome data, higher expression was associated with worse OS (HR = 1.22, 95% CI: 1.01-1.47, p = 0.036) and PFS (HR = 1.17, 95% CI: 1.00-1.37, p = 0.051) after adjusting for subgroup. Similar results were obtained when adjusting for metastatic status. Group 3 patients had the highest hTERT expression (p = 0.001). Sixty-one patients had pyrosequencing data, 292 had 450k methylation data. hTERT promoter was differentially methylated among subgroups with WNT followed by group 3 having the highest methylation on 450k (p < 0.0001). Pyrosequencing confirmed these trends. hTERT promoter methylation moderately positively correlated with hTERT expression (Spearman correlation = 0.42, p = 0.02 by 450k and 0.34, p = 0.007 by pyrosequencing). There was no correlation between expression of hTERT and c-MYC. CONCLUSION: Increased hTERT expression is associated with worse PFS and OS in MB regardless of subgroup, making telomerase a potential therapeutic target.
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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 | 0.001 |
| 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) | 0.000 | 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".