Cell Type of Pancreatic Ductal Adenocarcinoma Origin: Implications for Prognosis and Clinical Outcomes
Bibliographic record
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) is a devastating disease that has no effective early detection method or treatment to date. Summary: The normal cell type that initiates PDAC, or its cellular origin, is still unknown. To investigate the contribution of distinct normal epithelial cell types to PDAC tumorigenesis, genetically engineered mouse models were used to show that both acinar and ductal cells are capable of giving rise to PDAC. These studies indicated that genetic mutations and pancreatic injury interact differently with each cellular origin to affect their predilection and process for forming PDAC. In this review, we summarize recent findings using various genetically engineered mouse models in the identification and characterization of the PDAC cell of origin. We also discuss potential implications for cellular origin on tumor development, PDAC transcriptional subtype, and disease prognosis of patients. Key Message: Although it is clear that both ductal and acinar cells have the potential to form PDAC, whether cellular origin can indeed influence patient prognosis and whether knowledge of cellular origin will aid in the diagnosis or treatment of patients in the future will need further study.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".