EXTH-51. ENHANCING GLIOBLASTOMA TREATMENT: ALLOGENEIC CAR-T CELLS OVERCOMES FUNCTIONAL DEFICITS OF PATIENT-DERIVED AUTOLOGOUS PRODUCTS
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common primary brain malignancy in adults, with a dismal prognosis despite an intensive standard of care. Recently, chimeric antigen receptor T-cell (CAR-T) therapy has shown promising outcomes in treating liquid malignancies. However, clinical trials targeting various tumor antigens in GBM failed to show durable clinical benefit. Though this may stem from various tumor-intrinsic immune evasion strategies typical of GBM, there has been little work investigating whether the problem lies in the quality of the CAR-T products treatment itself. Currently, CAR-T cells for clinical studies are produced in an autologous setting, where T-cells are extracted from patients, engineered ex-vivo, and then re-infused. However, peripheral T-cells taken from GBM patients have shown qualitative and functional deficits, which may contribute to suboptimal treatment outcomes. Thus, we aimed to explore whether CAR-Ts generated from GBM patients had any functional deficits in comparison to healthy donors, utilizing our previously validated CD133 CAR-T. In this study, we show pre-treatment exhaustion, poor tumor control, and reduced survival advantage in autologous, patient-derived CD133-targeting CAR-T cell products using an orthotopic xenograft model of human GBM. To address the functional and logistical considerations of autologous therapy, we also sought to generate an “off-the-shelf” allogeneic CD133 CAR-T. Using CRISPR gene editing technology, we generated TCR-knockout CAR-T cells with comparable pre-clinical efficacy to our healthy donor derived autologous models. Ultimately, this work highlights the need to reevaluate autologous CAR-T therapy for GBM and consider allogeneic approaches as biologically-informed therapeutic alternatives.
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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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".