MétaCan
Menu
Back to cohort

Combined Immunotherapy of Dual-Targeted CAR NK Cells and Modified Oncolytic Virus Against Glioblastoma

2025· article· W4415469677 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueTheoretical and Natural Science · 2025
Typearticle
Language
FieldMedicine
TopicCAR-T cell therapy research
Canadian institutionsUniversity of Toronto
Fundersnot available
KeywordsOncolytic virusImmunotherapyChimeric antigen receptorTumor microenvironmentChemokineImmune systemGlioblastomaVirus

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.117
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.002
Science and technology studies0.0000.018
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.007
GPT teacher head0.285
Teacher spread0.278 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it