Engineering Dendritic Cells to Enhance Cancer Immunotherapy
Bibliographic record
Abstract
Cancer immunotherapy aims to establish immune-mediated control of tumor growth by priming T-cell responses to target tumor-associated antigens. Three signals are required for T-cell activation: (i) presentation of cognate antigen in self MHC molecules; (ii) costimulation by membrane-bound receptor-ligand pairs; and (iii) soluble factors to direct polarization of the ensuing immune response. The ability of dendritic cells (DCs) to provide all three signals required for T-cell activation makes them an ideal cancer vaccine platform. Several strategies have been developed to enhance and control antigen presentation, costimulation, and cytokine production. In this review, we discuss progress toward developing DC-based cancer vaccines by genetic modification using RNA, DNA, and recombinant viruses. Furthermore, the ability of DC-based vaccines to activate natural killer (NK) and B-cells, and the impact of gene modification strategies on these populations is described. Clinical trials using gene-modified DCs have shown modest results, therefore, further considerations for DC manipulation to enhance their clinical efficacy are also discussed.
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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 | 0.001 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".