Combined Immunotherapy of Dual-Targeted CAR NK Cells and Modified Oncolytic Virus Against Glioblastoma
Bibliographic record
Abstract
Glioblastoma (GBM) is a highly aggressive and treatment-resistant brain tumor with limited therapeutic options. This study explores a novel combined immunotherapy approach using dual-targeted CAR NK cells and a modified oncolytic virus (OV) to enhance anti-tumor efficacy. The CAR NK cells are engineered to target IL13Rα2 and CD19, while also incorporating IL6, IL21, and together with constitutively active STAT3 signalling to boost persistence and activity. The OV, derived from herpes simplex virus (HSV-1), is designed to express CD19 and the chemokine CCL5, facilitating NK cell recruitment and tumor targeting. Combined therapy in immunodeficient and immunocompetent mouse models shows significant tumor regression, prolonged survival, and increased immune cell infiltration compared to monotherapies. These results highlight the potential of this dual-mechanism strategy to overcome GBM’s immunosuppressive microenvironment and heterogeneity. However, challenges such as off-target effects on healthy B cells and testicular tissue warrant further investigation. This study provides a promising foundation for advancing combined CAR NK and OV therapies against GBM.
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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 | 0.000 |
| 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".