Cellular context determines <scp>DNA</scp> methylation profiles in <scp>SWI</scp>/<scp>SNF</scp>‐deficient cancers of the gynecologic tract
Bibliographic record
Abstract
SWI/SNF (SWItch/Sucrose Non-Fermentable) complex deficiency has been reported in a wide variety of cancers and is often associated with an undifferentiated phenotype. In the gynecologic tract SWI/SNF-deficient cancers are diagnostically challenging and little is known about their cellular origins. Here we show that undifferentiated endometrial carcinoma (UDEC), SMARCA4-deficient uterine sarcoma (SDUS), and ovarian small cell carcinoma, hypercalcemic type (SCCOHT) harbor distinct DNA methylation signatures despite shared morphology and SWI/SNF inactivation. Our results indicate that the cellular context is an important determinant of the epigenetic landscape, even in the setting of core SWI/SNF deficiency, and therefore methylation profiling may represent a useful diagnostic tool in undifferentiated, SWI/SNF-deficient cancers. Furthermore, applying copy number analyses and group-wise differential methylation analyses including endometrioid endometrial carcinomas and extracranial malignant rhabdoid tumors, we uncover analogous molecular features in SDUS and SCCOHT in contrast to UDEC. These results suggest that SDUS and SCCOHT represent chromosomally stable SWI/SNF-deficient cancers of the gynecologic tract, which are within the broader spectrum of malignant rhabdoid tumors. © 2022 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland.
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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 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".