Telomerase Activity as a Prognostic Factor in Neuroblastomas
Bibliographic record
Abstract
Neuroblastoma is the most common extracranial solid tumor of early childhood. This tumor demonstrates significant heterogeneity with respect to pathologic, genetic, and clinical features. The outcome varies from spontaneous regression or maturation to rapid progression, despite aggressive therapy. Prognostic factors have been found that identify those tumors which have a high probability of aggressive behavior; these factors include unfavorable histology, MYCN copy number, deletions of the short arm of chromosome 1, DNA content, and TRK-A (high-affinity receptor protein for nerve growth factor) expression. Recent studies have suggested that high levels of telomerase activity also correlate with poor clinical outcome. We investigated this relationship in 40 patients with untreated neuroblastoma, using a PCR-ELISA assay for telomerase activity. In these patients, 23 tumors had no or minimal telomerase activity whereas 15 had high levels of activity. In two tumors, telomerase activity was not assessable. There was significant correlation between the telomerase activity and MYCN copy number, 1p deletions, and TRK-A expression, as well as patient age, clinical stage, and outcome. The histological classification of the tumors was not significantly different between the two groups, being predominantly unfavorable by the Shimada classification. In addition, for 17 patients tumor tissue was assessed for telomerase activity post-chemotherapy. In those cases where the tumor was negative for telomerase activity before and after chemotherapy, the patients uniformly did well. In cases where the tumor was positive before and negative or weakly positive after treatment, two of the seven patients did well clinically. However, in cases that were positive after chemotherapy, all had recurrence or died. In conclusion, telomerase activity appears to be a prognostic factor for neuroblastomas. In addition, assessment of tumors post-chemotherapy may be a further indicator of clinical outcome.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".