Adaptation of the Th-MYCN Mouse Model of Neuroblastoma for Evaluation of Disseminated Disease
Bibliographic record
Abstract
High-risk neuroblastoma remains a profound clinical challenge that requires eradication of neuroblastoma cells from a variety of organ sites, including bone marrow, liver, and CNS, to achieve a cure. While preclinical modeling is a powerful tool for the development of novel cancer therapies, the lack of widely available models of metastatic neuroblastoma represents a significant barrier to the development of effective treatment strategies. To address this need, we report a novel luciferase-expressing derivative of the widely used Th-MYCN mouse. While our model recapitulates the non-metastatic neuroblastoma development seen in the parental transgenic strain, transplantation of primary tumor cells from disease-bearing mice enables longitudinal monitoring of neuroblastoma growth at distinct sites in immune-deficient or immune-competent recipients. The transplanted tumors retain GD2 expression through many rounds of serial transplantation and are sensitive to GD2-targeted immune therapy. With more diverse tissue localization than is seen with human cell line-derived xenografts, this novel model for high-risk neuroblastoma could contribute to the optimization of immune-based treatments for this deadly disease.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".