Context matters – Daxx and Atrx are not robust tumor suppressors in the murine endocrine pancreas
Bibliographic record
Abstract
Genome sequencing has revealed the importance of epigenetic regulators in tumorigenesis. The genes encoding the chromatin remodeling complex DAXX:ATRX are frequently mutated in pancreatic neuroendocrine tumors; however, the underlying mechanisms of how mutations contribute to tumorigenesis are only partially understood, in part because of the lack of relevant preclinical models. Here, we used genetically engineered mouse models combined with environmental stress to evaluate the tumor suppressor functions of Daxx and Atrx in the mouse pancreas. Daxx or Atrx loss, alone or in combination with Men1 loss, did not drive or accelerate pancreatic neuroendocrine tumorigenesis. Moreover, Daxx loss did not cooperate with environmental stresses (ionizing radiation or pancreatitis) or with the loss of other tumor suppressors (Pten or p53) to promote pancreatic neuroendocrine tumorigenesis. However, owing to promiscuity of the Cre promoter used, hepatocellular carcinomas and osteosarcomas were observed in some instances. Overall, our findings suggest that Daxx and Atrx are not robust tumor suppressors in the endocrine pancreas of mice and indicate that the context of a human genome is essential for tumorigenesis. This article has an associated First Person interview with the first author of the paper.
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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) | 0.000 | 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 | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.017 | 0.003 |
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".