Oncolytic virus–mediated expansion of dual-specific CAR T cells improves efficacy against solid tumors in mice
Bibliographic record
Abstract
Oncolytic viruses (OVs) encoding a variety of transgenes have been evaluated as therapeutic tools to increase the efficacy of chimeric antigen receptor (CAR)-modified T cells in the solid tumor microenvironment (TME). Here, using systemically delivered OVs and CAR T cells in immunocompetent mouse models, we have defined a mechanism by which OVs can potentiate CAR T cell efficacy against solid tumor models of melanoma and glioma. We show that stimulation of the native T cell receptor (TCR) with viral or virally encoded epitopes gives rise to enhanced proliferation, CAR-directed antitumor function, and distinct memory phenotypes. In vivo expansion of dual-specific (DS) CAR T cells was leveraged by in vitro preloading with oncolytic vesicular stomatitis virus (VSV) or reovirus, allowing for a further in vivo expansion and reactivation of T cells by homologous boosting. This treatment led to prolonged survival of mice with subcutaneous melanoma and intracranial glioma tumors. Human CD19 CAR T cells could also be expanded in vitro with TCR reactivity against viral or virally encoded antigens and was associated with greater CAR-directed cytokine production. Our data highlight the utility of combining OV and CAR T cell therapy and show that stimulation of the native TCR can be exploited to enhance CAR T cell activity and efficacy in mice.
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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) | 0.000 | 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) | 0.001 | 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".