CD4−8− Dendritic Cells Prime CD4+ T Regulatory 1 Cells to Suppress Antitumor Immunity
Bibliographic record
Abstract
It is clear that dendritic cells (DCs) are essential for priming of T cell responses against tumors. However, the distinct roles DC subsets play in regulation of T cell responses in vivo are largely undefined. In this study, we investigated the capacity of OVA-presenting CD4-8-, CD4+8-, or CD4-8+ DCs (OVA-pulsed DC (DC(OVA))) in stimulation of OVA-specific T cell responses. Our data show that each DC subset stimulated proliferation of allogeneic and autologous OVA-specific CD4+ and CD8+ T cells in vitro, but that the CD4-8- DCs did so only weakly. Both CD4+8- and CD4-8+ DC(OVA) induced strong tumor-specific CD4+ Th1 responses and fully protective CD8+ CTL-mediated antitumor immunity, whereas CD4-8- DC(OVA), which were less mature and secreted substantial TGF-beta upon coculture with TCR-transgenic OT II CD4+ T cells, induced the development of IL-10-secreting CD4+ T regulatory 1 (Tr1) cells. Transfer of these Tr1 cells, but not T cells from cocultures of CD4-8- DC(OVA) and IL-10-/- OT II CD4+ T cells, into CD4-8+ DC(OVA)-immunized animals abrogated otherwise inevitable development of antitumor immunity. Taken together, CD4-8- DCs stimulate development of IL-10-secreting CD4+ Tr1 cells that mediated immune suppression, whereas both CD4+8- and CD4-8+ DCs effectively primed animals for protective CD8+ CTL-mediated antitumor immunity.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.002 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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; both teacher heads agree on what is shown here.
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".