Oncolytic Maraba Virus MG1 Mediates Direct and Natural Killer Cell-Dependent Lysis of Ewing Sarcoma
Bibliographic record
Abstract
BACKGROUND: Ewing sarcoma (EWS) is a rare cancer of the bone and soft tissue, most prevalent in children and young adults. The treatment of EWS has progressed relatively little in over 30 years. Survival rates for patients, particularly those with metastatic and/or relapsed disease remain poor, highlighting the urgent need for innovative treatment options. METHODS: Here, we have explored the therapeutic potential of the oncolytic Maraba virus strain MG1 using various in vitro models of EWS, including established cell lines, doxorubicin-resistant derivatives, spheroid cultures and primary patient-derived Ewing sarcoma cell cultures. We examined the direct oncolytic activity of MG1 and its ability to stimulate the immune-mediated killing of EWS by human healthy donor peripheral blood mononuclear cells. RESULTS: We show that MG1 undergoes productive replication and exerts direct oncolysis of established EWS cell lines, doxorubicin-resistant EWS cell lines and patient-derived Ewing sarcoma cell cultures more recently established from tumours. In contrast, primary mesenchymal stem cells (the likely cell of origin of EWS) were resistant to MG1, with IFN-I being a major determinant of tumour cell selectivity. MG1-treated PBMC produced IFN-I and killed EWS cells in vitro, in a natural killer (NK) cell-dependent manner. CONCLUSIONS: The ability of MG1 to kill EWS cells directly and stimulate NK cell cytotoxicity against this tumour suggests that MG1 may provide therapeutic benefit for EWS patients where the efficacy of conventional treatments is currently limited.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".