Efficient antitumor immunity derived from maturation of dendritic cells that had phagocytosed apoptotic/necrotic tumor cells
Bibliographic record
Abstract
Dendritic cells (DCs) that acquired antigen from apoptotic tumor cells are able to induce major histocompatibility complex (MHC) class I-restricted cytotoxic T lymphocytes and antitumor immunity. In the present study, we investigated the efficiency of antitumor immunity derived from DCs that had phagocytosed apoptotic/necrotic BL6-10 melanoma cells compared with that of DCs pulsed with the tumor mTRP2 peptide. Our data showed that phagocytosis of apoptotic/necrotic tumor cells resulted in maturation of DCs with up-regulated expression of proinflammatory cytokines [interleukin (IL)-1beta, IL-6, tumor necrosis factor-alpha, interferon-gamma and granulocyte-macrophage colony-stimulating factor], chemokines (MIP-1alpha, MIP-1beta and MIP-2), the CC chemokine receptor CCR7 and the cell surface molecules (MHC class II, CD11b, CD40 and CD86), and down-regulated expression of the CC chemokine receptors CCR2 and CCR5. These mature DCs displayed enhanced migration toward the CC chemokine MIP-3beta in a chemotaxis assay in vitro and to the regional lymph nodes in an animal model in vivo. Our data also showed that vaccination with DCs that had phagocytosed apoptotic/necrotic BL6-10 cells was able to (i) more strongly stimulate allogeneic T-cell proliferation in vitro, (ii) induce an in vivo Th1-type immune response leading to more efficient tumor-specific cytotoxic CD8(+) T-cell-mediated immunity and (iii) eradicate lung metastases in all 6 vaccinated mice compared with mice vaccinated with DCs pulsed with the tumor mTRP2 peptide, in which lung metastases were reduced (mean number of 16 per mouse) but not completely eradicated. Therefore, DCs that had phagocytosed apoptotic/necrotic tumor cells appear to offer new strategies in DC cancer vaccines.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".