MYCC and MYCN Oncogene Amplification in Medulloblastoma
Bibliographic record
Abstract
Abstract Context. —Brain tumors are the most common solid tumor in childhood, and medulloblastoma is the most common malignant brain tumor in this age group. Cytogenetic abnormalities that have been described in childhood medulloblastoma include loss of 17p, amplification of MYCC (c- myc ), amplification of MYCN (N- myc ), and isochromosome 17q. Data on these tumors indicate that the frequency of MYCC amplification is 5% to 10%. Fluorescence in situ hybridization is a powerful tool for investigating these features on archival material. Objectives. —To determine if intratumoral heterogeneity exists for MYCC and MYCN in medulloblastomas and if tumors with amplified MYCC or MYCN exhibit consistent histologic patterns. Design. —In this fluorescence in situ hybridization study, we investigated the frequency and prognostic significance of MYCC and MYCN amplification in 77 medulloblastomas derived from the Children's Oncology Group. Results. — MYCC amplification occurred in only 4 (5.2%) of 77 tumors. The 4 patients died of clinically aggressive neoplasms within 7 months of diagnosis. Similarly, 4 of 77 patients' tumors were found to exhibit MYCN amplification, but survival data are incomplete at present, therefore prognostic significance cannot be characterized. Conclusions. —These data establish the frequency of MYCC amplification in a large cohort of children with medulloblastoma and further suggest that MYCC amplification may be a marker of poor prognosis. Intratumoral heterogeneity was identified for these oncogenes in that 1 patient's tumor exhibited evidence of both MYCN and MYCC amplification, and this patient experienced a shortened survival time.
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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 | 0.001 | 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) | 0.001 | 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".