Three-dimensional Nuclear Telomere Architecture Is Associated with Differential Time to Progression and Overall Survival in Glioblastoma Patients
Bibliographic record
Abstract
The absence of biological markers allowing for the assessment of the evolution and prognosis of glioblastoma (GBM) is a major impediment to the clinical management of GBM patients. The observed variability in patients' treatment responses and in outcomes implies biological heterogeneity and the existence of unidentified patient categories. Here, we define for the first time three GBM patient categories with distinct and clinically predictive three-dimensional nuclear-telomeric architecture defined by telomere number, size, and frequency of telomeric aggregates. GBM patient samples were examined by three-dimensional fluorescent in situ hybridization of telomeres using two independent three-dimensional telomere-measurement tools (TeloView program [P1] and SpotScan system [P2]). These measurements identified three patients categories (categories 1–3), displaying significant differences in telomere numbers/nucleus (P1 = .0275; P2 ≤ .0001), telomere length (P1 and P2 = .0275), and number of telomeric aggregates (P1 = .0464; P2 ≤ .0001). These categories corresponded to patients with long-term, intermediate, and short-term survival, respectively (P = .0393). The time to progression analyses showed significant differences between the three categories (P = .0167). There was a correlation between time to progression, median survival, and nuclear telomere architecture. Our study suggests a link between patient outcome and three-dimensional nuclear-telomere organization and highlights the potential clinical power of telomere signatures as a new prognostic, predictive, and potentially pharmacodynamic biomarker in GBM. Furthermore, novel automated three-dimensional high-throughput scanning as developed here permits to obtain data from 300 nuclei in 20 minutes. This method is applicable to any cell type and scanning application.
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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".