Conditional Activation of c-MYC in Distinct Catecholaminergic Cells Drives Development of Neuroblastoma or Somatostatinoma
Bibliographic record
Abstract
c-MYC is an important driver of high-risk neuroblastoma. A lack of c-MYC-driven genetically engineered mouse models (GEMM) has hampered the ability to better understand mechanisms of neuroblastoma oncogenesis and to develop effective therapies. In this study, we showed that conditional c-MYC induction via Cre recombinase driven by a tyrosine hydroxylase promoter led to a preponderance of PDX1+ somatostatinoma, a type of pancreatic neuroendocrine tumor. However, c-MYC activation via an improved Cre recombinase driven by a dopamine β-hydroxylase promoter resulted in neuroblastoma development. The c-MYC murine neuroblastoma tumors recapitulated the pathologic and genetic features of human neuroblastoma and responded to anti-GD2 immunotherapy and difluoromethylornithine, an FDA-approved inhibitor targeting the MYC transcriptional target ODC1. Thus, c-MYC overexpression results in different but related tumor types depending on the targeted cell. The GEMMs represent valuable tools for testing immunotherapies and targeted therapies for these diseases. Significance: The development of c-MYC-driven genetically engineered neuroblastoma and somatostatinoma mouse models provides useful tools for understanding the tumor cell origin and investigating treatment strategies.
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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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".