Dendritic Cell-Based Vaccines Recruit Neutrophils to the Local Draining Lymph Nodes to Prime Natural Killer Cell Responses
Bibliographic record
Abstract
Dendritic cell (DC)-based cancer vaccines are a form of immunotherapy that activates the innate and adaptive immune systems to combat cancers. Neutrophils contribute to cancer biology and have the potential to be exploited by immunotherapeutic platforms to enhance anti-tumor immune responses. We previously showed that DC vaccines elicit the expansion of mouse interferon (IFN)γ-producing mature natural killer (NK) cells to elevate anti-tumor responses. Here, we demonstrate the rapid recruitment of neutrophils to the draining lymph nodes of DC-vaccinated mice. This was accompanied by an increase in the total number of NK cells producing IFNγ and expressing CD107a, a marker of degranulation that demonstrates NK cell functional activity. Furthermore, the depletion of neutrophils in DC-immunized mice resulted in decreased numbers of NK cells in draining lymph nodes compared to the controls. Interestingly, the increased number of IFNγ- and CD107a-expressing NK cells in DC-immunized mice was not detected in mice depleted of neutrophils. Further investigations showed that DC vaccines induced IFNγ- and TNFα-producing CD8+ T cells that also expressed CD107a, but depletion of neutrophils did not have any impact on the CD8+ T cell population. Our findings suggest that neutrophil-mediated anti-tumor immunity induced by a DC vaccine platform could be targeted to provide innovative strategies to enhance its clinical efficacy.
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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) | 0.001 | 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".