Strategies to Enhance the Efficacy of T-Cell Therapy for Central Nervous System Tumors
Bibliographic record
Abstract
Mortality rates in patients diagnosed with central nervous system (CNS) tumors, originating in the brain or spinal cord, continue to remain high despite the advances in multimodal treatment regimens, including surgery, radiation, and chemotherapy. Recent success of adoptive cell transfer immunotherapy treatments using chimeric antigen receptor (CAR) engineered T cells against in chemotherapy resistant CD19 expressing B-cell lymphomas, has provided the foundation for investigating efficacy of CAR T immunotherapies in the context of brain tumor. Although significant efforts have been made in developing and translating the novel CAR T therapies for CNS tumors, including glioblastoma (GBM), researchers are yet to achieve a similar level of success as with liquid malignancies. In this review, we discuss strategies and considerations essential for developing robust preclinical models for the translation of T cell-based therapies for CNS tumors. Some of the key considerations include route of delivery, increasing persistence of T cells in tumor environment, remodeling of myeloid environment, establishing the window of treatment opportunity, harnessing endogenous immune system, designing multiple antigen targeting T cells, and rational combination of immunotherapy with the current standard of care. Although this review focuses primarily on CAR T therapies for GBM, similar strategies, and considerations are applicable to all CNS tumors in general.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".