Tumor Dendritic Cells (DCs) Derived from Precursors of Conventional DCs Are Dispensable for Intratumor CTL Responses
Bibliographic record
Abstract
Abstract The success of adoptive CTL therapy for cancer depends on interactions between tumor-infiltrating CTLs and cancer cells as well as other cells and molecules in the tumor microenvironment. Tumor dendritic cells (DCs) comprise several subsets: CD103+CD11b− DC1 and CD11b+CD64− DC2, which originate from circulating precursors of conventional DCs, and CD11b+CD64+ DC3, which arise from monocytes. It remains controversial which of these subset(s) promotes intratumor CTL proliferation, expansion, and function. To address this issue, we used the Zbtb46-DTR–transgenic mouse model to selectively deplete DC1 and DC2 from tumors and lymphoid tissues. Wild-type and Zbtb46-DTR bone marrow chimeras were inoculated with B16 melanoma cells that express OVA and were treated with OT-1 CTLs. We found that depletion of DCs derived from precursors of conventional DCs in Zbtb46-DTR bone marrow chimeras abolished CTL proliferation and expansion in tumor-draining lymph nodes. By contrast, intratumor CTL accumulation, proliferation, and IFN-γ expression were unaffected by their absence. We found that adoptive cell therapy increases the frequency of monocyte-derived tumor DC3, which possess the capacity to cross-present tumor Ags and induce CTL proliferation. Our findings support the specialized roles of different DC subsets in the regulation of antitumor CTL responses.
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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".