<i>Acvr1b</i> loss increases formation of pancreatic precancerous lesions from acinar and ductal cells of origin
Bibliographic record
Abstract
Abstract Background & Aims Pancreatic ductal adenocarcinoma (PDAC) can develop from precursor lesions, including pancreatic intraepithelial neoplasia (PanIN) and intraductal papillary mucinous neoplasm (IPMN). Previous studies indicated that loss of Acvr1b accelerates the Kras-mediated development of papillary IPMN in the mouse pancreas, however, the cell type predominantly affected by these genetic changes remains unclear. Methods We investigated the contribution of cellular origin by inducing IPMN associated mutations-KRAS G12D expression and Acvr1b loss - specifically in acinar ( Ptf1a CreER ;Kras LSL-G12D ;Acvr1b fl/fl mice) or ductal ( Sox9CreER;Kras LSL- G12D ;Acvr1b fl/fl mice) cells in mice. We then performed MRI imaging and a thorough histopathological analysis of their pancreatic tissues. Results The loss of Acvr1b increased the development of PanIN and IPMN-like lesions when either acinar and ductal cells expressed a Kras mutation. MRI, immunohistochemistry and histology revealed large IPMN-like lesions in these mice that exhibited features of flat, gastric epithelium. In addition, cyst formation in both mouse models was accompanied by chronic pancreatitis. Experimental acute pancreatitis accelerated the development of large mucinous cysts and PanIN when acinar, but not ductal, cells expressed mutant Kras and lost Acvr1b . Conclusion These findings indicate that loss of Acvr1b in the presence of the Kras oncogene promotes the development of large and small precancerous lesions from both ductal and acinar cells. However, the IPMN-like phenotype was not equivalent to that observed when these mutations were made in all pancreatic cells during development. Our study underscores the significance of the cellular context in the initiation and progression of precursor lesions from exocrine cells.
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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 | 0.000 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".