Vaccination of Dendritic Cells Loaded with Interleukin-12-Secreting Cancer Cells Augments <i>In vivo</i> Antitumor Immunity: Characteristics of Syngeneic and Allogeneic Antigen-Presenting Cell Cancer Hybrid Cells
Bibliographic record
Abstract
Cancer immunotherapy by fusion of antigen-presenting cells and tumor cells has been shown to induce potent antitumor immunity. In this study, we characterized syngeneic and allogeneic, murine macrophage/dendritic cell (DC)-cancer fusion cells for the antitumor effects. The results showed the superiority of allogeneic cells as fusion partners in both types of antigen-presenting cells in an in vivo immunotherapy model. A potent induction of tumor-specific CTLs was observed in these immunized conditions. In addition, the immunization with DC-cancer fusion cells was better than that with macrophage-cancer fusion cells. Both syngeneic and allogeneic DC-cancer fusion cells induced higher levels of IFN-gamma production than macrophage-cancer fusion cells. Interestingly, allogeneic DC-cancer fusion cells were superior in that they efficiently induced Th1-type cytokines but not the Th2-type cytokines interleukin (IL)-10 and IL-4, whereas syngeneic DC-cancer fusion cells were powerful inducers of both Th1 and Th2 cytokines. These results suggest that allogeneic DCs are suitable as fusion cells in cancer immunotherapy. To further enhance the antitumor immunity in the clinical setting, we prepared DCs fused with IL-12 gene-transferred cancer cells and thus generated IL-12-secreting DC-cancer fusion cells. Immunization with these gene-modified DC-cancer fusion cells was able to elicit a markedly enhanced antitumor effect in the in vivo therapeutic model. This novel IL-12-producing fusion cell vaccine might be one promising intervention for future cancer immunotherapy.
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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".