Recently characterized molecular events in uncommon gynaecological neoplasms and their clinical importance
Bibliographic record
Abstract
The introduction of new sequencing technologies has resulted in the discovery of commonly mutated genes in uncommon cancers, including non-epithelial ovarian neoplasms and other rare gynaecological tumours, such as cervical embryonal rhabdomyosarcoma. In some of these neoplasms, mutations in certain genes are both frequent and specific enough for the genomic mutations and sometimes their associated protein loss or overexpression to be used as an aid to diagnosis. In this review, we contrast previous gene identification methods with newer ones, and discuss how the new sequencing technologies (collectively referred to as 'next-generation sequencing') have permitted the identification of specific molecular events that characterize several rare gynaecological neoplasms. We highlight the value of using sequencing to complement traditional pathological methods when diagnosing certain tumours, and provide practical advice to pathologists dealing with these neoplasms. We focus on adult granulosa cell tumours (somatic monoallelic mutations in FOXL2), Sertoli-Leydig cell tumours, gynaecological embryonal rhabdomyosarcomas (germline and somatic mutations in DICER1), and small-cell carcinoma of the ovary, hypercalcaemic type (biallelic mutations in SMARCA4). The new genetic findings provided by next-generation sequencing in these uncommon neoplasms have brought these disorders back into focus, and point the way towards new diagnostic, preventive and therapeutic avenues.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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 teacher head, 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".