Daxx Functions Are p53-Independent <i>In Vivo</i>
Bibliographic record
Abstract
Abstract Mutations in the death domain–associated protein (DAXX) have been recently identified in a substantial proportion of human pancreatic neuroendocrine tumors (PanNETs). Remarkably, however, little is known about the physiologic role(s) of DAXX despite in vitro studies suggesting potential functions. Most prominently, and supported by tumor sequencing data, DAXX functions in concert with alpha thalassemia/mental retardation X-linked (ATRX) as a histone chaperone complex for the H3.3 variant. Studies have also identified potential roles in apoptosis, transcription, and negative regulation of the p53 tumor suppressor pathway. Herein, a mouse modeling approach was used to specifically address the latter and no significant genetic interaction between Daxx and the p53 pathway was determined. The embryonic lethal phenotype of Daxx loss is not p53-dependent. In addition, Daxx heterozygosity does not sensitize mice to a sublethal dose of ionizing radiation or alter the survival or tumor phenotype of Mdm2 transgenic mice. However, the data support a tumor suppressor role for DAXX as low-dose ionizing radiation produced a higher proportion of carcinomas in Daxx heterozygous mice than wild-type controls. Implications: While DAXX has important in vivo functions, they are independent of an inhibitory role on the p53 tumor suppressor pathway. Mol Cancer Res; 16(10); 1523–9. ©2018 AACR.
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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 | 0.000 |
| 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) | 0.004 | 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".