Recruitment and Differentiation of Conventional Dendritic Cell Precursors in Tumors
Bibliographic record
Abstract
The origin of dendritic cells (DCs) in tumors remains obscure. Recent studies indicate that conventional DCs (cDCs) in lymphoid tissues arise from a distinct population of committed cDC precursors (pre-cDCs) that originate in bone marrow and migrate via blood. In this study, we show that pre-cDCs are precursors for cDCs in tumors. Pre-cDCs from tumors, bone marrow, and spleen exhibit similar morphologic, immunophenotypic, and functional properties. Adoptive transfer studies show that bone marrow pre-cDCs migrate from blood into the tumor where they generate cDCs. The chemokine CCL3, which is markedly upregulated in tumors, promotes pre-cDC recruitment. Both pre-cDCs and their cDC progeny actively proliferate within the tumor. cDCs that arise from pre-cDCs in tumors express lower levels of CD11c and MHC class II as compared with those in spleen; however, there was no difference in their abilities to respond to maturation stimuli or activate Ag-specific lymphocytes in vitro. Our study provides the first evidence supporting a role for pre-cDCs in DC development in tumors and suggests a potential target for 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 | 0.000 |
| 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".