Dendritic Cell-Based Cancer Vaccines: The Impact of Modulating Innate Lymphoid Cells on Anti-Tumor Efficacy
Bibliographic record
Abstract
Dendritic cell (DC) vaccines stimulate the immune system to target cancer antigens, representing a promising option for immunotherapy. However, clinical trials have demonstrated limited effectiveness, emphasizing the need for enhanced immune responses. Improving the production of DC vaccines, assessing their impact on immune components, and observing responses could improve the results of DC-based therapies. Innate lymphoid cells (ILCs) represent a heterogeneous population of innate immune components that generate cytokines and modulate the immune system, potentially enhancing immunotherapies. Recent research highlights the different functions of ILCs in cancer, demonstrating their dual capabilities to promote tumors and exhibit anti-tumor actions. DCs and ILCs actively communicate under physiological and pathological conditions, and the activation of ILCs by DCs or DC vaccines has been shown to influence ILC cytokine production and function. Gaining insights into the interaction between DC-activated ILCs and tumors is essential for creating exciting new therapeutic strategies. These strategies aim to boost anti-tumor immunity while reducing the support that tumors receive. This review examines the effect of DC vaccination on host ILCs, illustrating the complex relationship between DC-based vaccines and ILCs. Furthermore, it explores some exciting strategies to enhance DC vaccines, aiming to boost anti-tumor immune responses by fostering better engagement with ILCs.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".