Three-Dimensional Nuclear Telomere Profiling as a Biomarker for Recurrence in Oligodendrogliomas: A Pilot Study
Bibliographic record
Abstract
Mechanisms of recurrence in oligodendrogliomas are poorly understood. Recurrence might be driven by telomere dysfunction-mediated genomic instability. In a pilot study, we investigated ten patients with oligodendrogliomas at the time of diagnosis (first surgery) and after recurrence (second surgery) using three-dimensional nuclear telomere analysis performed with quantitative software TeloView® (Telo Genomics Corp, Toronto, Ontario, Canada). 1p/19q deletion status of each patient was determined by fluorescent in situ hybridization on touch preparation slides. We found that a very specific 3D telomeric profile was associated with two pathways of recurrence in oligodendrogliomas independent of their 1p/19q status: a first group of 8 patients displayed significantly different 3D telomere profiles between both surgeries (p < 0.0001). Their recurrence happened at a mean of 231.375 ± 117.42 days and a median time to progression (TTP) of 239 days, a period defined as short-term recurrence; and a second group of three patients displayed identical 3D telomere profiles between both surgery samples (p > 0.05). Their recurrence happened at a mean of 960.666 ± 86.19 days and a median TTP of 930 days, a period defined as long-term recurrence. Our results suggest a potential link between nuclear telomere architecture and telomere dysfunction with time to recurrence in oligodendrogliomas, independently of the 1p/19q status.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".