Defining the molecular mechanisms that control the development of tumor dendritic cells. (66.32)
Bibliographic record
Abstract
Abstract Cancer-associated inflammation contributes to tumor progression and metastasis. Defective dendritic cell (DC) function causes impaired adaptive immunity to antigens expressed by tumors. We recently found that the intra-tumor inflammatory milieu recruits and skews development of DC precursors (pre-cDC) towards a novel Gr-1+ subpopulation, which has tolerogenic properties. This transformation reduced the proliferation and expansion of antigen-specific cytotoxic T cells in tumors. We sought to identify cytokines that regulate this differentiation. Isolated pre-cDC from mice spleen was incubated with tumor-conditioned medium (TCM) derived from Lewis lung carcinoma cell line. Supernatants from cultures were collected to analyze cytokine levels. Expression of Gr-1+ on DC was monitored by flow cytometry. We found that TCM induced pre-cDC to differentiate into Gr-1+ cDC. Neutralizing anti-cytokine Ab studies revealed that IL-6 play a role in promoting Gr-1+ cDC differentiation. Pre-cDC from IL-6-/- mice showed a reduced capacity to develop into Gr-1+ cDC. TCM contained no IL-6, but it induced IL-6 secretion by pre-cDC. These findings suggested that the release of autocrine IL-6 by pre-cDC created a positive feedback loop with tumor-derived factors in TCM to promote Gr-1cDC development. Elucidation of the mechanisms by which tumor-derived factors communicate with pre-cDC will advance our understanding of the mechanisms involved in the development of impaired immune response to cancer.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".