Tumor-derived factors promote differentiation of immunosuppressive Gr-1+cDC through TLR2. (127.25)
Bibliographic record
Abstract
Abstract Tumor cells secrete endogenous danger signals that modulate the tumor inflammatory microenvironment. We have found that the intra-tumor inflammatory milieu skews differentiation of DC precursors (pre-cDC) towards a novel immunosuppressive Gr-1+ subpopulation. Inhibition of this differentiation enhanced cytotoxic T cells expansion and suppressed tumor growth. We sought to identify signaling mechanisms that regulate Gr-1+DC development. Splenic Pre-cDC from wildtype and MyD88-/- mice were incubated with tumor-conditioned medium (TCM) derived from the Lewis lung carcinoma cell line or fibroblast control medium. Supernatants were collected to measure various cytokine levels and cell surface expression of Gr-1 was monitored by flow cytometry. We found that TCM, but not control medium induced IL-6, IL-10 & IL-1β production and Gr-1+DC development. These cells produced high amounts of IL-10 after secondary stimulation with LPS. MyD88-/- Pre-cDC stimulated with TCM did not produce cytokines or differentiate into Gr-1+DC. Tumors from MyD88-/- mice contained a significantly lower frequency of Gr-1+DC compared to wildtype mice (14.1% vs. 1.1%; p=0.001, n>5). To determine whether Toll-like receptor 2 (TLR2) was involved in Gr-1+DC development, we stimulated Pre-cDC with different TLR agonists. We found that both Pam3CSK4 (TLR2/TLR1) and FSL-1 (TLR2/TLR6), but not LPS (TLR4) mimicked the activity of TCM. Together these data suggest that MyD88 and TLR2 are involved in Gr-1+DC development.
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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) | 0.003 | 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".