Abstract A19: Immunologic dysfunctions of NSG mice confer higher engraftment levels of xenograft Ewing sarcoma metastasis in the PuMA model compared to NOD-SCID mice
Bibliographic record
Abstract
Abstract Introduction: Ewing sarcoma is characterized by expression of EWS-ETS fusion proteins generated by chromosomal translocations and comprises bone and/or soft tissue tumors affecting mainly children and young adults. The majority of patients respond well to conventional therapy, but 5-year overall survival of patients with pulmonary metastasis at diagnosis or patients with recurrence remains dismal. The pulmonary metastasis assay (PuMA) is an ex vivo lung explant system developed to study the biology of lung colonization in osteosarcoma (OS), using fluorescence microscopy. The most significant among several advantage of the PuMA model over conventional assays for metastasis is that it recapitulates the initial stages of lung colonization (detachment from primary tumor, intravasation to blood circulation, and arrest to the secondary site) and allows the assessment of antimetastatic effects of a given intervention (candidate gene knockdown or drug treatment) in a 3D microenvironment. For unknown reasons, Ewing sarcoma cell lines do not survive lung colonization using NOD-SCID mice in the PuMA model. NSG mice are generated by crossing NOD-SCID with IL2γ receptor null mice (NOD.Cg-Prkdcscid IL2rgtm1Wjl/SzJ). These further immunocompromised mice have emerged as successful models to study breast cancer and melanoma metastasis. We herein compared metastatic engraftment of Ewing sarcoma A673 cells in NOD-SCID versus NSG mice in the PUMA model. Methods: Metastatic tumor growth was accessed using PuMA. Briefly, 1 × 106 viable A673 dTomato labeled tumor cells were injected into NOD-SCID or NSG mice via tail veins. Mice were then euthanized, chest cavities exposed, and tracheas cannulated, and PneumaCult-ALI medium (Stem Cell Tech) mixed with 1.2% agarose was infused into the lung. Lung slices of ~1-2mm from each lung lobe were incubated in 6 well plates with PneumaCult-ALI (day 0). Lung slice images were captured on days 0, 3, 7, and 14 using a Leica Stereo Microscope to evaluate tumor growth and H&E staining. Results: In NOD-SCID mice, A673 cell did not form metastatic lesions 3 days after successful injection. In NSG mice, tumor cells were able to successfully colonize the lungs (fold change of tumor burden = 1, 1.5, and 3.2 on day 0, 3, and 7, respectively). Conclusion: Our results show that NSG mice confer higher engraftment levels of xenografted A673 cells in PuMA assays compared to NOD-SCID. NSG and NOD-SCID mice share T- and B-cell depletion and loss of C5 complement, but NSG mice present extremely low NK activity and innate immunity. NOD-SCID may rely on functionally immature macrophages and residual NK activity to target Ewing sarcoma cells. Better understanding specific immunologic response in Ewing sarcoma patients and the mechanisms by which Ewing tumor cells evade immune system may help the development of novel therapy able to prevent advanced disease. Citation Format: Renata Scopim-Ribeiro, Michael M. Lizardo, Anne-Chloe Dhez, Amal M. El-Naggar, Poul H. Sorensen. Immunologic dysfunctions of NSG mice confer higher engraftment levels of xenograft Ewing sarcoma metastasis in the PuMA model compared to NOD-SCID mice [abstract]. In: Proceedings of the AACR Special Conference on the Advances in Pediatric Cancer Research; 2019 Sep 17-20; Montreal, QC, Canada. Philadelphia (PA): AACR; Cancer Res 2020;80(14 Suppl):Abstract nr A19.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
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".