Toll-like Receptor 2 Activation Promotes Tumor Dendritic Cell Dysfunction by Regulating IL-6 and IL-10 Receptor Signaling
Bibliographic record
Abstract
Although dendritic cell (DC) dysfunction in cancer is a well-recognized consequence of cancer-associated inflammation that contributes to immune evasion, the mechanisms that drive this process remain elusive. Here, we show the critical importance of tumor-derived TLR2 ligands in the generation of immunosuppressive IL-10-producing human and mouse DCs. TLR2 ligation induced two parallel synergistic processes that converged to activate STAT3: stimulation of autocrine IL-6 and IL-10 and upregulation of their respective cell surface receptors, which lowered the STAT3 activation threshold. We identified versican as a soluble tumor-derived factor that activates TLR2 in DCs. TLR2 blockade in vivo improved intra-tumor DC immunogenicity and enhanced the efficacy of immunotherapy. Our findings provide a basis for understanding the molecular mechanisms of DC dysfunction in cancer and identify TLR2 as a relevant therapeutic target to improve cancer immunotherapy.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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".