Advances in Dendritic Cell-Based Vaccine of Cancer
Bibliographic record
Abstract
Dendritic cells (DCs) are potent antigen presenting cells that exist in virtually every tissue, and from which they capture antigens and migrate to secondary lymphoid organs where they activate naïve T cells. Although DCs are normally present in extremely small numbers in the circulation, recent advances in DC biology have allowed the development of methods to generate large numbers of these cells in vitro. Because of their immunoregulatory capacity, vaccination with tumor antigen-presenting DCs has been proposed as a treatment modality for cancer. In animal models, vaccination with DCs pulsed with tumor peptides, lysates, or RNA or loaded with apoptotic/necrotic tumor cells could induce significant antitumor CTL responses and antitumor immunity. However, the results from early clinical trails pointed to a need for additional improvement of DC-based vaccines before they could be considered as practical alternatives to the existing cancer treatment strategies. In this regard, subsequent studies have shown that DCs that express transgenes encoding tumor antigens are more potent primers of antitumor immunity both in vitro and in vivo than DCs simply pulsed with tumor peptides. Furthermore, DCs that have been engineered to express certain cytokines or chemokines can display a substantially improved maturation status, capacity to migrate to secondary lymphoid organs in vivo, and abilities to stimulate tumor-specific T cell responses and induce tumor immunity in vivo. In this review we also discuss a number of factors that are important considerations in designing DC vaccine strategies, including (i) the type and concentrations of tumor peptides used for pulsing DCs; (ii) the timing and intervals for DC vaccination/boostable data on DC vaccination portends bright prospects for this approach to tumor immune therapy, either alone or in conjunction with other therapies.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 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 | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".