The antitumoral activity of TLR7 ligands is corrupted by the microenvironment of pancreatic tumors
Bibliographic record
Abstract
Abstract Toll like receptors are key players in the innate immune system. Recent studies have suggested that they may impact the growth of pancreatic cancer, a disease with no cure. Among them, Toll like receptor-7 shows promise for therapy but may also promote tumor growth. Thus, we aimed to better understand the mechanism of action of Toll like receptor-7 ligands in pancreatic cancer, to open the door for clinical applications. In vitro , Toll like receptor-7 ligands strongly inhibit the proliferation and induce cell death by apoptosis of pancreatic cancer cells. In vivo , while Toll like receptor-7 agonists significantly delay the growth of aggressive tumors engrafted in immunodeficient mice, they instead surprisingly promote tumor growth and accelerate animal death in immunocompetent models. Molecular investigations revealed that Toll like receptor-7 agonists strongly increase the number of tumor-promoting macrophages to drive pancreatic tumorigenesis in immunocompetent mice. This is in stark contrast with Toll like receptor-7 ligands’ great potential to inhibit pancreatic cancer cell proliferation in vitro and tumor growth in vivo in immunosuppressed models. Collectively, our findings shine a light on the duality of action of Toll like receptor-7 agonists in experimental cancer models, and calls into question their use for pancreatic cancer therapy.
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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".