Using oncolytic viruses to exploit endogenous TCR signaling for modulation of CAR T phenotype and function against solid tumors 4322
Bibliographic record
Abstract
Abstract Description We used C57BL/6 mice bearing B16-F10 tumors expressing EGFRviii, treated with anti-EGFRviii CAR T cells and the oncolytic virus vesicular stomatitis virus (VSV). TCR signaling in CAR T cells was investigated using the novel Tocky transgenic mouse model, which reports T cell activation over time using an unstable fluorescent protein reporter of Nr4a3. We further investigated CAR T phenotypes using CyTOF and scRNA-Seq. Using an MHC I tetramer for the dominant VSV epitope VSV-N52-59, we found that 20-50% of transfused CAR T adopted TCR specificity against VSV-N (TCR-primed), far above the predicted frequency in a naïve T cell repertoire. Ex vivo, these TCR-primed CAR T produced increased levels of Granzyme B and IFNγ over tetramer negative CAR T against either TCR or CAR targets. TCR-primed CAR had decreased levels of T cell activation as compared to non-TCR-primed CAR. CyTOF and scRNA-Seq analyses demonstrated a hyper-effector phenotype among TCR-primed CAR, with increased expression of T-bet, CD44, Granzyme B, IFNγ, and perforin. TCR sequencing revealed hyper-expansion of VSV N-specific CAR T cells and little overlap in clonotypes between CAR T co-treated with PBS versus VSV. Overall, CAR T cells that undergo endogenous anti-viral TCR priming and signaling appear to adopt a unique phenotype that promotes their cytotoxic function. These data suggest that engineering anti-viral TCRs into CAR T could be used to improve efficacy with OV boosting. Funding Sources Supported by NIH T32GM145408 and NIH R01CA269384. Topic Categories Vaccines and Immunotherapy (VAC)
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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 | 0.001 |
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 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".