Endometrial neuroendocrine carcinoma and undifferentiated carcinoma are distinct entities with overlap in neuroendocrine marker expression
Bibliographic record
Abstract
AIMS: Dedifferentiated endometrial carcinomas (DDECs)/undifferentiated endometrial carcinomas (UDECs) frequently harbour genomic activation of switch/sucrose non-fermentable (SWI/SNF)-complex proteins, and can show histological overlap with neuroendocrine carcinoma (NEC). The aim of this study was to compare the extent of the expression of neuroendocrine markers, SWI/SNF proteins and mismatch repair (MMR) proteins in DDEC/UDEC and NEC. METHODS AND RESULTS: The extent of expression of synaptophysin, chromogranin, CD56, ARID1A, ARID1B, SMARCA4, SMARCB1 and MMR proteins was evaluated by immunohistochemistry on 44 SWI/SNF-deficient DDECs/UDECs and 15 NECs. Thirty-three of 44 (75%) DDECs/UDECs showed expression of at least one neuroendocrine marker, with 18 of 44 (41%) expressing two or more neuroendocrine markers, whereas all 15 NECs showed expression of at least one neuroendocrine marker, with 14 of 15 (93%) expressing two or more neuroendocrine markers. Neuroendocrine marker expression in DDECs/UDECs was typically focal when present, with average extents of 17%, 4% and 8% for synaptophysin, chromogranin and CD56 in the positive cases, respectively, in contrast to 73%, 40% and 62% in the positive NEC cases, respectively. All 15 NECs showed intact expression of SWI/SNF-complex proteins, except for one that showed isolated loss of ARID1A. Thirty-eight of 44 DDECs/UDECs were MMR-abnormal (34 with loss of MLH1 and PMS2, and four with loss of PMS2 alone), whereas all NECs retained MMR protein expression. CONCLUSIONS: Our study demonstrates frequent but typically focal neuroendocrine marker expression in SWI/SNF-deficient DDECs/UDECs, whereas NECs typically express two or more neuroendocrine markers, with diffuse expression of at least one marker. ARID1B, SMARCA4 and SMARCB1 immunohistochemistry can be used to aid in the differentiation between DDEC/UDEC and NEC.
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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 | 0.000 | 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) | 0.000 | 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".