Anaplastic rhabdomyosarcoma in <i>TP53</i> germline mutation carriers
Bibliographic record
Abstract
BACKGROUND: Rhabdomyosarcoma (RMS) represents a diverse category of myogenic malignancies with marked differences in molecular alterations and histology. This study examines the question if RMS predisposition due to germline TP53 mutations correlates with certain RMS histologies. METHODS: The histology of RMS tumors diagnosed in 8 consecutive children with TP53 germline mutations was reviewed retrospectively. In addition, germline TP53 mutation analysis was performed in 7 children with anaplastic RMS (anRMS) and previously unknown TP53 status. RESULTS: RMS tumors diagnosed in 11 TP53 germline mutation carriers all exhibited nonalveolar, anaplastic histology as evidenced by the presence of enlarged hyperchromatic nuclei with or without atypical mitotic figures. Anaplastic RMS was the first malignant diagnosis for all TP53 germline mutation carriers in this cohort, and median age at diagnosis was 40 months (mean, 40 months ± 15 months; range, 19-67 months). The overall frequency of TP53 germline mutations was 73% (11 of 15 children) in pediatric patients with anRMS. The frequency of TP53 germline mutations in children with anRMS was 100% (5 of 5 children) for those with a family cancer history consistent with Li-Fraumeni syndrome (LFS), and 80% (4 of 5 children) for those without an LFS cancer phenotype. CONCLUSIONS: Individuals harboring germline TP53 mutations are predisposed to develop anRMS at a young age. If future studies in larger anRMS cohorts confirm the findings of this study, the current Chompret criteria for LFS should be extended to include children with anRMS irrespective of family history.
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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 | 0.000 |
| 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) | 0.002 | 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".