A Universal Boosting Strategy for Adoptive T-cell Therapy Using a Paired Vaccine/Chimeric Antigen Receptor
Bibliographic record
Abstract
Vaccines that encode tumor-associated antigens are potent boosting agents for adoptively transferred tumor-specific T cells. Employing vaccines to boost adoptively transferred tumor-reactive T cells relies on a priori knowledge of tumor epitopes, isolation of matched epitope-specific T cells, and personalized vaccines, all of which limit clinical feasibility. In this study, we investigated a universal strategy for boosting transferred tumor-specific T cells for which boosting is provided through a chimeric antigen receptor (CAR) that is paired with a vaccine encoding the CAR target antigen. To this end, we developed and employed a model in which murine T cells expressing a T-cell receptor (TCR) specific for antigen on syngeneic tumors were engineered with boosting CARs against a distinct surrogate boosting antigen for studies in immunocompetent hosts. Boosting CAR-engineered tumor-specific T cells with paired vesicular stomatitis virus vaccines was associated with robust T-cell expansion and delayed tumor progression in the absence of prior lymphodepletion. CAR T-cell expansion and antitumor function were further enhanced by blocking IFNAR1. However, vaccine-boosted CAR T cells rapidly contracted and antigen-positive tumors re-emerged. In contrast, when the same T cells were boosted with a vaccine encoding antigen that stimulates through the TCR, the adoptively transferred T cells displayed improved persistence, tumor-specific endogenous cells expanded in parallel, and tumor cells carrying the antigen target were completely eradicated. Our findings underscore the need for further research into CAR-mediated vaccine boosting, how this differs mechanistically from TCR-mediated boosting, and the importance of engaging endogenous tumor-reactive T cells during vaccination to achieve long-term tumor control.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 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) | 0.003 | 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 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".