NSG Mice Facilitate ex vivo Characterization of Ewing Sarcoma Lung Metastasis Using the PuMA Model
Bibliographic record
Abstract
Ewing sarcoma (EwS) is a highly malignant bone and soft tissue tumor primarily affecting children and young adults. While most patients initially respond well to conventional front-line therapy, frequent metastasis results in poor 5-year overall survival rates for this disease. Accordingly, there is a critical need to develop better models to understand EwS metastasis. We and others previously used the ex vivo pulmonary metastasis assay (PuMA) to study lung metastasis in solid tumors including osteosarcoma (OS), but this technique has to date not been achievable for EwS. PuMA involves tail vein injection of fluorescent tumor cells into NOD-SCID mice, followed by their visualization in long-term cultures of tumor-bearing lung explants. Here we demonstrate successful implementation of PuMA for EwS cells using NOD-SCID-IL2 receptor gamma null (NSG) immunocompromised mice, which demonstrated high engraftment of EwS cell lines compared to NOD-SCID mice. This may be linked to immune permissiveness required by EwS cells, as increased basal cytotoxicity of EwS cells was observed in NOD-SCID compared to NSG lung sections, possibly due to the absence of natural killer (NK) cell activity in the latter. Together, our data demonstrate the utility of NSG mice for PuMA modeling of EwS lung metastasis.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".