Targeting Apoptotic Tumor Cells to FcγR Provides Efficient and Versatile Vaccination Against Tumors by Dendritic Cells
Bibliographic record
Abstract
Dendritic cells (DCs) loaded with tumor-associated Ags (TAAs) act as potent adjuvant that initiates antitumor immune responses in vivo. However, TAA-based DC vaccination requires prior identification of TAAs. Apoptotic tumor cells (ATCs) can be an excellent source for DC loading because their potential uncharacterized Ags would be efficiently presented to T cells without any prior characterization and isolation of these Ags. However, ATCs alone are considered to be inefficient for activating antitumor immunity, possibly because of their inability to induce DC maturation. In this study, the aim was to enhance antitumor immune response by taking advantage of ATCs that have been opsonized with IgG (ATC-immune complexes, ATC-ICs) so as to target them to FcR for IgG (FcgammaRs) on DCs. It was found that when compared with ATCs, ATC-ICs were efficiently internalized by DCs via FcgammaRs, and this process induced maturation of DCs, which was more efficient than that of ATCs. Importantly, ATC-IC loading was shown to be more efficient than ATCs alone in its capacity for inducing antitumor immunity in vivo, in terms of cytotoxic T cell induction and tumor rejection. These results show that using ATC-ICs may overcome the limitations and may enhance the immune response of current ATC-based DC vaccination therapy.
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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 | 0.000 |
| 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".