No small surprise – small cell carcinoma of the ovary, hypercalcaemic type, is a malignant rhabdoid tumour
Bibliographic record
Abstract
Whole-exome sequencing (WES) is revolutionizing medical diagnostics and taxonomy. In less than 5 years since its first use, WES has revealed unexpected molecular drivers of numerous cancers. Here, we describe our use of WES to uncover the true nature of an enigmatic pathological entity, small-cell carcinoma of the ovary, hypercalcaemic type (SCCOHT), which has resisted definitive characterisation since it was first described in 1979. We conducted WES using three families with SCCOHT and identified deleterious mutations in the chromatin-remodelling gene SMARCA4 (encoding BRG1) in all cases. Follow-up of these findings, using both Sanger sequencing and WES of formalin-fixed paraffin-embedded tumours, showed that virtually all SCCOHTs we studied lacked functional SMARCA4/BRG1. Notably, this gene, and the related SMARCB1 gene, is mutated in most, if not all, atypical teratoid/rhabdoid tumours and malignant rhabdoid tumours. Other groups have similar findings. We review the relationship between these three neoplasms, discuss how they were distinguished from morphologically similar neoplasms, consider their similarities and show how WES has revealed that SCCOHTs are in fact rhabdoid tumours. We propose that SCCOHT be renamed 'malignant rhabdoid tumour of the ovary' (MRTO) to reflect these observations.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".