Loss of Expression of DNA Mismatch Repair Proteins Is Rare in Pancreatic and Small Intestinal Neuroendocrine Tumors
Bibliographic record
Abstract
CONTEXT: Two recent studies have identified a high rate of microsatellite instability (MSI) in pancreatic neuroendocrine tumors (pNETs). Microsatellite instability is rare in small intestinal neuroendocrine tumors (NETs). It is unclear why there is discordance in the frequency of MSI in the 2 studies of pNETs and why this mechanism is comparatively rare in small intestinal tumors. Loss of expression of DNA mismatch repair (MMR) proteins, which is known to correlate strongly with MSI, is not well studied in pancreatic or small intestinal NETs. OBJECTIVE: To determine if there is loss of expression of MMR protein expression in pancreatic or small intestinal NETs. DESIGN: Sixty-nine patients (31 male, 38 female; mean age, 59.2 years) were identified who had a resection for a primary pancreatic (n = 35) or primary small intestinal (n = 34) NET during an 18-year period. Immunohistochemical stains for MLH1, MSH2, MSH6, and PMS2 were applied to archived tissue from all cases. All pNETs with adequate tissue (n = 32) were also assessed by MSI analysis. RESULTS: There was preserved expression of MLH1, MSH2, MSH6, and PMS2 in all 35 pNETs. Of 32 pNETs tested by polymerase chain reaction, 28 were microsatellite stable and DNA did not amplify in 4. In 34 small intestinal NETs, 2 cases had indeterminate MLH1 and 1 case had indeterminate PMS2 expression. The remainder had intact MMR protein expression. CONCLUSION: Defects in DNA MMR proteins are rare in pancreatic and small intestinal NETs, raising doubt that MSI plays a significant role in the pathogenesis of these tumors.
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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 | 0.002 |
| 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".