Loss of ARID1B and SMARCB1 expression are specific for the diagnosis of dedifferentiated/undifferentiated carcinoma in tumours of the upper gynaecological tract and cervix
Bibliographic record
Abstract
AIMS: Genomic inactivation of ARID1B in ARID1A-inactivated tumour and genomic inactivation of SMARCB1 represent two recurrent mechanisms, core SWItch/sucrose non-fermentable (SWI/SNF) complex inactivation, that are associated with de-differentiation in endometrial carcinoma. Approximately one-third of dedifferentiated/undifferentiated endometrial carcinomas (DDEC/UEC) show loss of ARID1B expression with a minor subset showing loss of SMARCB1 expression, but little is known regarding the specificity of ARID1B or SMARCB1 loss in gynaecological tract tumours in general. The aim of this study was to examine the frequency of ARID1B and SMARCB1 loss by immunohistochemistry in a series of gynaecological tract epithelial/mesenchymal neoplasms. METHODS AND RESULTS: We evaluated 1849 tumours that included 748 endometrial carcinomas, 101 uterine carcinosarcomas/adenosarcomas, 64 uterine sarcomas, 221 cervical carcinomas and 715 ovarian carcinomas/borderline tumours by tissue microarrays (TMA). We observed ARID1B loss in 35 of 86 (41%) and SMARCB1 loss in three of 86 (3%) DDEC/UEC, but not in any other uterine tumour types examined. ARID1B-deficient DDEC/UEC also showed concurrent loss of ARID1A expression. All SMARCB1-deficient tumours showed loss of MLH1 and PMS2, while 29 of 35 ARID1B-deficient tumours showed loss of MLH1 and PMS2 or loss of MSH6. All ovarian carcinomas/borderline tumours and cervical carcinomas showed intact expression of ARID1B and SMARCB1. CONCLUSION: Our findings indicate that the loss of expression of ARID1B or SMARCB1 by immunohistochemistry is highly specific for undifferentiated carcinoma among tumours of the upper gynaecological tract and cervix, and therefore can be used to identify these highly aggressive malignant tumours.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".