Mouse models for pancreatic ductal adenocarcinoma are affected by the cre-driver used to promote KRAS <sup>G12D</sup> activation
Bibliographic record
Abstract
Abstract The fundamental biology of pancreatic ductal adenocarcinoma has been greatly impacted by the characterization of genetically modified mouse models that allow temporal and spatial activation of oncogenic KRAS (KRAS G12D ). The most commonly used model involves targeted insertion of a cre recombinase into the Ptf1a gene. However, this approach disrupts the Ptf1a gene, resulting in haploinsufficiency that likely affects sensitivity to oncogenic KRAS ( KRAS G12D ). The goal of this study was to determine if Ptf1a haploinsufficiency affected the acinar cell response to KRAS G12D before and after induction of pancreatic injury. We performed morphological and molecular analysis of three mouse lines that express a tamoxifen-inducible cre recombinase to activate KRAS G12D in acinar cells of the pancreas. The cre-recombinase was targeted to the acinar-specific transcription factor genes, Ptf1a and Mist1/Bhlha15 , or expressed within a BAC-derived Elastase transgene. Up to two months after tamoxifen induction of KRAS G12D , morphological changes were negligible. However, induction of pancreatic injury by cerulein resulted in stark differences in tissue morphology between lines within seven days, which were maintained for at least five weeks after injury. Ptf1a creERT pancreata showed widespread PanIN lesions and fibrosis, while the Mist1 creERT and Ela-creERT models showed reduced amounts of pre-neoplastic lesions. RNA-seq analysis prior to inducing injury suggested Ptf1a creERT and Mist1 creERT lines have unique profiles of gene expression that predict a differential response to injury. Multiplex analysis of pancreatic tissue confirmed different inflammatory responses between the lines. These findings suggest understanding the mechanisms underlying the differential response to KRAS G12D will help in further defining the intrinsic KRAS-driven mechanisms of neoplasia initiation.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 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".