<i>Mist1-KrasG12D</i> Knock-In Mice Develop Mixed Differentiation Metastatic Exocrine Pancreatic Carcinoma and Hepatocellular Carcinoma
Bibliographic record
Abstract
Despite the prevalence of oncogenic Kras mutations in the earliest stages of pancreatic ductal adenocarcinoma, the cellular compartment in which oncogenic Kras initiates tumorigenesis remains unknown. To address this, we have gene targeted KrasG12D into the open reading frame of Mist1, a basic helix-loop-helix transcription factor that is expressed during pancreatic development and required for proper pancreatic acinar organization. Although the pancreata of Mist1(KrasG12D/+) mutant mice predictably exhibited acinar metaplasia and dysplasia, the frequent death of these mice from invasive and metastatic pancreatic cancer with mixed histologic characteristics, including acinar, cystic, and ductal features, was unexpected and in contrast to previously described mutant mice that ectopically expressed the Kras oncogene in either acinar or ductal compartments. Interestingly, many of the mutant mice developed hepatocellular carcinoma, implicating Mist1(KrasG12D/+) cells in both pancreatic and hepatic neoplasia. Concomitant Trp53+/- mutation cooperated with Mist1(KrasG12D/+) to accelerate lethality and was associated with advanced histopathologic findings, including parenchymal liver metastasis. These findings suggest that Mist1-expressing cells represent a permissive compartment for transformation by oncogenic Kras in pancreatic tumorigenesis.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".