Identification of Neuroblastoma Subgroups Based on Three-Dimensional Telomere Organization
Bibliographic record
Abstract
Using 3D telomere quantitative fluorescence in situ hybridization, we determined the 3D telomere organization of 74 neuroblastoma tissue samples. Hierarchical cluster analysis of the measured telomere parameters identified three subgroups from our patient cohort. These subgroups have unique telomere profiles based on telomere length and nuclear architecture. Subgroups with higher levels of telomere dysfunction were comprised of tumors with greater numbers of telomeres, telomeric aggregates, and short telomeres (P<.0001). Tumors with greater telomere dysfunction were associated with unfavorable tumor characteristics (greater age at diagnosis, unfavorable histology, higher stage of disease, MYCN amplification, and higher MYCN expression) and poor prognostic risk (P<.001). Subgroups with greater telomere dysfunction also had higher intratumor heterogeneity. MYCN overexpression in two neuroblastoma cell lines with constitutively low MYCN expression induced changes in their telomere profile that were consistent with increased telomere dysfunction; this illustrates a functional relationship between MYCN and 3D telomere organization. This study demonstrates the ability to classify neuroblastomas based on the level of telomere dysfunction, which is a novel approach for this cancer.
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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 | 0.001 | 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".