Loss of switch/sucrose non‐fermenting complex protein expression in undifferentiated gastrointestinal and pancreatic carcinomas
Bibliographic record
Abstract
AIMS: Undifferentiated carcinoma refers to an epithelial malignancy that lacks morphological evidence of differentiation. Recent studies have implicated the loss of constitutively expressed switch/sucrose non-fermenting (SWI/SNF) complex subunits in undifferentiated carcinomas of the gastrointestinal tract and other sites. In this study we examine the expression of SWI/SNF and mismatch repair (MMR) proteins in a series of undifferentiated carcinomas from the gastrointestinal tract and the pancreas. METHODS AND RESULTS: We searched pathology databases from four Canadian health centres for primary undifferentiated carcinoma from gastrointestinal and pancreatic resection specimens. Upon review of 31 cases, 19 were confirmed to be undifferentiated carcinomas (eight colonic, six gastric, three pancreatic, one appendiceal and one duodenal). Immunohistochemical analysis of SMARCA4, SMARCA2, SMARCB1, ARID1A, ARID1B, MSH2, MSH6, MLH1 and PMS2 was performed on whole sections. Five of 19 (26%) showed loss of core SWI/SNF proteins (two loss of SMARCA4, one loss of SMARCB1 and two concurrent loss of ARID1A and ARID1B). SMARCA4, SMARCB1, or ARID1A/ARID1B-deficient undifferentiated carcinoma consistently exhibited sheet-like growth pattern, with cellular discohesion and rhabdoid morphology. Nine of 17 undifferentiated carcinomas tested were MMR-deficient by immunohistochemistry. In comparison, none of the 12 poorly differentiated carcinomas that were originally diagnosed as undifferentiated carcinomas showed loss of SMARCA4, SMARCA2, SMARCB1 or ARID1B. CONCLUSIONS: Undifferentiated gastrointestinal/pancreatic carcinomas show frequent loss of expression of SWI/SNF complex proteins. The loss of these core components of SWI/SNF complex may contribute to the arrest of cellular differentiation, resulting in the undifferentiated histology and aggressive clinical behaviour.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| 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".