EXTH-64. Allogeneic CAR-DNT cell therapy to overcome spatial heterogeneity in glioblastoma
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common primary malignant brain tumour, with a 5-year survival rate under 10%. Chimeric antigen receptor (CAR)-T cell therapy shows promise, but tumour heterogeneity – especially between the tumour core and infiltrative edge – complicates target selection. Edge cells are particularly challenging to eliminate due to their migration into healthy brain tissue. Further, the cost and complexity of manufacturing autologous CAR-T therapy limit accessibility. This study explores an innovative allogeneic CAR-T strategy capable of targeting multiple tumour subpopulations, improving the efficacy and accessibility of CAR-T treatment for GBM. We utilised double-negative T (DNT) cells (CD3+CD4-CD8-), which exhibit innate anti-tumour cytotoxic activity and are suitable for allogeneic use without inducing graft-versus-host disease (GvHD). DNTs were engineered with second-generation 4-1BB CARs targeting CD276 or CD19 (negative control). To evaluate efficacy, we developed a novel orthotopic xenograft model using human brain tumour-initiating cells (BTICs) isolated from spatially distinct tumour regions – core (X) and edge (Z). Fluorescently labelled X- and Z-BTICs were implanted intracranially at a 1:1 ratio. CAR-DNTs demonstrated CAR transduction efficiency and in vitro cytotoxicity comparable to conventional CD4+/CD8+ T (Tconv) cells. In vivo, αCD276 CAR-DNTs and CAR-Tconv cells similarly extended survival in the XZ GBM model. However, only CAR-Tconv treatment induced severe xenogeneic GvHD. Notably, all αCD276 CAR groups showed full or predominant clearance of Z-edge cells at relapse. Additionally, DNTs mediated CAR-independent cytotoxicity of X-core BTICs in vitro compared to Tconv cells. Ongoing studies are investigating whether eradication of Z-edge cells depends on the presence of X-core cells and potential inter-population crosstalk. We will also assess whether the edge-targeting capacity of αCD276 CARs with the core-targeting effects of DNTs can offer a comprehensive solution to overcome GBM spatial heterogeneity. αCD276 CAR-DNTs hold promise for a more effective, accessible, and scalable treatment paradigm for GBM, ultimately transforming patient care.
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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".