Genomic profiling of dedifferentiated endometrial carcinomas arising in the background of high‐grade carcinoma: a targeted next‐generation sequencing study
Bibliographic record
Abstract
AIMS: Our understanding of dedifferentiated endometrial carcinoma (DEC), a rare and aggressive malignancy, mainly reflects undifferentiated carcinomas (UC) arising in the setting of low-grade endometrial cancer (DEC-LG). However, cases of UC arising in the setting of high-grade EC (DEC-HG) have been noted in the literature. Our knowledge of the genomics of DEC-HG is limited. To characterise the molecular landscape of DEC-HC, targeted genomic sequencing and immunohistochemical analysis was carried out on seven DEC-HG and four DEC-LG. METHODS AND RESULTS: DEC-HG and DEC-LG, including undifferentiated and differentiated components, both showed a similar frequency and spectrum of mutations. ARID1A mutations were identified in 6/7 (86%) DEC-HG and 4/4 (100%) DEC-LG, while SMARCA4 mutations were present in 4/7 (57%) DEC-HG and in 1/4 (25%) DEC-LG. Concurrent SMARCA4/BRG1 protein loss by immunohistochemistry was observed in 3/4 and 1/1 SMARCA4 mutated DEC-HG and DEC-LG, respectively. Neither genomic alterations nor protein loss in SMARCB1/INI1 were observed in any of our cases. TP53 mutations were detected in 4/7 (57%) DEC-HG and in 2/4 (50%) DEC-LG, while mutation-pattern p53 immunohistochemistry expression was observed in 2/7 (29%) DEC-HG and none of the DEC-LG. MLH1 mutations were observed in 1/7 (14%) DEC-HG and 1/4 (25%) DEC-LG. MSH2 and MSH6 mutations were each detected in 1/7 (14%) DEC-HG, but neither was associated with corresponding loss of protein expression. CONCLUSION: The findings support expanding the definition of DEC to include DEC-HG, a previously under-recognised phenomenon with genomic similarities to DEC-LG.
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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 | 0.001 | 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 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".