Recombinant Vesicular Stomatitis Virus Transduction of Dendritic Cells Enhances Their Ability to Prime Innate and Adaptive Antitumor Immunity
Bibliographic record
Abstract
Dendritic cell (DC)-based vaccines are a promising strategy for tumor immunotherapy due to their ability to activate both antigen-specific T-cell immunity and innate immune effector components, including natural killer (NK) cells. However, the optimal mode of antigen delivery and DC activation remains to be determined. Using M protein mutant vesicular stomatitis virus (DeltaM51-VSV) as a gene-delivery vector, we demonstrate that a high level of transgene expression could be achieved in approximately 70% of DCs without affecting cell viability. Furthermore, DeltaM51-VSV infection activated DCs to produce proinflammatory cytokines (interleukin-12, tumor necrosis factor-alpha, and interferon (IFN)alpha/beta), and to display a mature phenotype (CD40(high)CD86(high) major histocompatibility complex (MHC II)(high)). When delivered to mice bearing 10-day-old lung metastatic tumors, DCs infected with DeltaM51-VSV encoding a tumor-associated antigen mediated significant control of tumor growth by engaging both NK and CD8(+) T cells. Importantly, depletion of NK cells completely abrogated tumor destruction, indicating that NK cells play a critical role for this DC vaccine-induced therapeutic outcome. Our findings identify DeltaM51-VSV as both an efficient gene-delivery vector and a maturation agent allowing DC vaccines to overcome immunosuppression in the tumor-bearing host.
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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 | 0.000 |
| 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".