A modular and flexible ESC-based mouse model of pancreatic cancer
Bibliographic record
Abstract
Genetically engineered mouse models (GEMMs) have greatly expanded our knowledge of pancreatic ductal adenocarcinoma (PDAC) and serve as a critical tool to identify and evaluate new treatment strategies. However, the cost and time required to generate conventional pancreatic cancer GEMMs limits their use for investigating novel genetic interactions in tumor development and maintenance. To address this problem, we developed flexible embryonic stem cell (ESC)-based GEMMs that facilitate the rapid generation of genetically defined multiallelic chimeric mice without further strain intercrossing. The ESCs harbor a latent Kras mutant (a nearly ubiquitous feature of pancreatic cancer), a homing cassette, and other genetic elements needed for rapid insertion and conditional expression of tetracycline-controlled transgenes, including fluorescence-coupled shRNAs capable of efficiently silencing gene function by RNAi. This system produces a disease that recapitulates the progression of pancreatic cancer in human patients and enables the study and visualization of the impact of gene perturbation at any stage of pancreas cancer progression. We describe the use of this approach to dissect temporal roles for the tumor suppressor Pten and the oncogene c-Myc in pancreatic cancer development and maintenance.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".