AUTOLOGOUS PATIENT-DERIVED CAR-TS SHOW FUNCTIONAL DEFICITS AGAINST HUMAN GLIOBLASTOMA
Bibliographic record
Abstract
Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults, with a poor prognosis despite aggressive standard of care. Chimeric antigen receptor T-cell (CAR-T) therapy has shown promising results in liquid malignancies, but clinical trials in GBM targeting various tumor antigens have not shown durable clinical benefit. While this may be attributable to various tumor-intrinsic immune evasion strategies characteristic of GBM, little work has assessed whether the issue is due to the quality of the CAR-T treatment itself. Currently, CAR-Ts for GBMs and liquid malignancies are manufactured in an autologous setting in which T-cells are extracted from patients, engineered ex-vivo, and subsequently reinfused back. However, peripheral T-cells taken from untreated GBM patients have demonstrated qualitative and functional deficits, which may contribute to suboptimal treatment outcomes. Thus, we aimed to establish whether CAR-Ts generated from GBM patients would show reduced efficacy in comparison to healthy donors using our previously validated CD133 CAR-T. In this work, we show pre-treatment exhaustion and reduced survival advantage in autologous, patient-derived CD133-targeting CAR-T cell products using an orthotopic xenograft model of human GBM. To overcome the functional and logistical considerations of autologous therapy, we additionally aimed 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 autologous models. Ultimately, this work highlights the need to reassess 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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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) | 0.001 | 0.001 |
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 teacher head, 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".