Enhanced Antitumor Immunity Elicited by Dendritic Cell Vaccines Is a Result of Their Ability to Engage Both CTL and IFNγ-producing NK Cells
Bibliographic record
Abstract
Activation of cytotoxic T lymphocytes (CTLs) is a primary goal of many cancer vaccination therapies. We have evaluated two vaccination platforms, dendritic cells (DCs) and recombinant adenoviruses (rAds), for their ability to elicit CTL response and antitumor protection. Although rAd was more potent in CTL priming, DC vaccination provided greater protective and therapeutic antitumor activity. Subsequent analyses ruled out the possibility that the two vaccines elicit qualitatively distinct CTL, and demonstrated instead that DCs could better engage natural killer (NK) cells as an additional effector mechanism. We demonstrated that, although both DCs and rAd can stimulate rapid NK expansion, only DC-activated NK cells are able to produce interferon-gamma (IFN gamma) and mediate antitumor protection. Moreover, our data showed that exogenously delivered DCs preferentially engaged the Mac-1(high)CD27(high) NK subset, thereby suggesting that this NK population plays a predominant role in NK:DC interaction. Interestingly, at least 3 days were required for DC-triggered NK cells to acquire effector functions, indicating that a similar priming process operates between T cells and NK cells. Our results suggest that the nature of the vaccine platform can determine the relative involvement of NK and T cells in antitumor immunity, and that methods to augment NK function should be included in vaccination strategies in order to complement CTL-mediated control of tumor growth.
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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) | 0.001 | 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".