Ovarian embryonal rhabdomyosarcoma is a rare manifestation of the DICER1 syndrome
Bibliographic record
Abstract
Abstract Embryonal rhabdomyosarcoma (ERMS), a malignant soft tissue sarcoma, is one of the most common paediatric cancers. Certain ERMS tumours are associated with the DICER1 syndrome, a distinctive tumour predisposition syndrome caused by germ-line mutations in the microRNA-maturation pathway gene, DICER1 . In addition to germ-line DICER1 mutations, highly characteristic somatic mutations have been identified in several DICER1-associated tumour types. These so-called “hotspot” mutations affect highly conserved amino acid residues central to the catalytic activity of the DICER1 ribonuclease IIIb domain. Primary ovarian ERMS (oERMS) is extremely rare. We present a case of a 6-year-old girl with an oERMS found to harbour two mutations in DICER1 . In addition to the oERMS, the girl also exhibited other DICER1 phenotypes, including cystic nephroma (CN) and multinodular goitre. Somatic investigations of the CN revealed the presence of a hotspot DICER1 mutation different from that in the oERMS. Of particular interest is the CN presented at the age of 12 years, which is much older than previously reported age range of susceptibility (birth to four years of age). This report documents both germ-line and highly characteristic somatic DICER1 mutations in a case of oERMS, adding to the expanding spectrum of rare childhood tumours in the DICER1 syndrome.
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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) | 0.001 | 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 | 0.001 | 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".