Baseline AHR expression shapes immune response to pharmacological modulation in PBMCs from pancreatic cancer patients
Bibliographic record
Abstract
Background: Pancreatic ductal adenocarcinoma (PDAC) remains largely unresponsive to immunotherapy because of its highly immunosuppressive tumor microenvironment. Aryl hydrocarbon receptor (AHR), a ligand-dependent transcription factor, has emerged as a key regulator of immune homeostasis and inflammation. However, its systemic immunomodulatory role in PDAC, particularly outside the tumor microenvironment, remains poorly understood. Methods: with two AHR agonists (Carbidopa and Tapinarof) and one antagonist (BAY 2416964). The samples were stratified into Low and High/Medium AHR expression groups. Flow cytometry (FC), qPCR, ELISA, Luminex assays, and immunofluorescence imaging were used to evaluate immune checkpoint expression, cytokine secretion, monocyte polarization, and subcellular AHR localization. Overall survival analysis was performed based on the baseline AHR expression levels. Results: Baseline AHR expression strongly influenced the immunological effects of AHR modulators. In High/Medium AHR PBMCs, Carbidopa increased PD-L1 and soluble PD-1 (sPD-1) levels, while IL10 expression was suppressed. In contrast, BAY significantly reduced PD-1 and sPD-1 levels in Low AHR PBMCs, whereas Tapinarof induced the highest IL10 expression. All modulators reduced the proportion of M2-like monocytes, indicating a shift toward less immunosuppressive phenotypes. Nuclear translocation of AHR protein varied across treatments and expression levels. Kaplan-Meier analysis revealed a non-significant trend toward improved overall survival in the High/Medium AHR group (log-rank p = 0.276). Conclusion: Baseline AHR expression critically shapes the immune response to pharmacological modulation in PBMCs from PDAC patients. These findings suggest that AHR profiling may serve as a clinically relevant biomarker for stratifying patients and guiding personalized immunotherapy approaches for PDAC.
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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) | 0.002 | 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".