The Role of Telomere Maintenance in the Spontaneous Growth Arrest of Pediatric Low-Grade Gliomas
Bibliographic record
Abstract
Spontaneous tumor regression of pediatric low-grade gliomas (PLGG). We speculated that lack of telomere maintenance is responsible for this behavior. We first looked for evidence of telomerase activity and alternative lengthening of telomeres (ALT) in 56 PLGG. Telomerase activity was observed in 0 of 11 PLGG, in contrast to 10 of 13 high-grade pediatric brain tumors. There was no ALT in 45 of 45 samples. We then applied Q-FISH to eight patients whose indolent PLGG underwent two metachronous biopsies over a lag of several years. Telomere shortening was observed in the second biopsy in all tumors, but not in normal brain control (P < .0001), indicating that lack of telomere maintenance is associated with continuous telomere erosion. Based on these observations, we found that younger PLGG patients, who exhibit more aggressive and frequently recurrent tumors, had significantly longer telomeres than older ones (P = .00014). Tumors with a terminal restriction fragment length <7.5 did not recur, whereas the presence of longer telomeres (>8.0) conferred a high likelihood of late recurrences in PLGG. Our findings provide a plausible biologic mechanism to explain the tendency of PLGG to exhibit growth arrest and spontaneous regression. Telomere maintenance may therefore represent the first known biologic prognostic marker in PLGG.
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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) | 0.001 | 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".