<i>DICER1</i> screening in 15 paediatric paratesticular sarcomas unveils an unusual DICER1‐associated sarcoma
Bibliographic record
Abstract
Individuals with DICER1 syndrome, a genetic disorder caused by pathogenic germline variants in DICER1, are at increased risk of developing a wide array of predominantly childhood onset conditions, including genitourinary sarcomas. However, data on DICER1 involvement in paratesticular sarcomas have not been published. Herein, we analyse a series of 15 paediatric paratesticular sarcomas and describe in detail the case of a male infant with a paratesticular myxoid tumour, considered to be a low-grade sarcoma, who also manifested a cystic nephroma, a classic DICER1 syndrome phenotype. He harboured a pathogenic germline DICER1 variant and different somatic hot-spot mutations in each tumour. The paratesticular tumour showed strong and diffuse expression for WT1 and CD10, an unusual immunophenotype in paediatric sarcomas, but typical of tumours of Müllerian origin. The tumour was postulated to arise from the appendix testis, a Müllerian remnant located in the paratestis. Such an origin would be analogous to other DICER1-associated non-epithelial gynaecological tumours, thought to arise from Müllerian derivatives. These findings point towards a key role of DICER1 in Müllerian-derived structures. Supporting this hypothesis is the fact that the other paratesticular sarcomas from the series were either negative or focally positive for WT1 and for CD10, and none had any DICER1 mutations. In summary, we present the first case of a paratesticular sarcoma associated with DICER1 syndrome, emphasising that paratesticular tumours with an unusual histological appearance may suggest an underlying DICER1 mutation, especially in the presence of a personal or family history of DICER1-associated disease. In this context, DICER1 mutation testing could lead to changes in clinical care including implementation of cancer care surveillance strategies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".