Tumor Cell–Derived IL1β Promotes Desmoplasia and Immune Suppression in Pancreatic Cancer
Bibliographic record
Abstract
Abstract Pancreatic ductal adenocarcinoma (PDA) is an aggressive malignancy typified by a highly stromal and weakly immunogenic tumor microenvironment that promotes tumor evolution and contributes to therapeutic resistance. Here, we demonstrate that PDA tumor cell–derived proinflammatory cytokine IL1β is essential for the establishment of the protumorigenic PDA microenvironment. Tumor cell–derived IL1β promoted the activation and secretory phenotype of quiescent pancreatic stellate cells and established an immunosuppressive milieu mediated by M2 macrophages, myeloid-derived suppressor cells, CD1dhiCD5+ regulatory B cells, and Th17 cells. Loss of tumor cell–derived IL1 signaling in tumor stroma enabled intratumoral infiltration and activation of CD8+ cytotoxic T cells, attenuated growth of pancreatic neoplasia, and conferred survival advantage to PDA-bearing mice. Accordingly, antibody-mediated neutralization of IL1β significantly enhanced the antitumor activity of α-PD-1 and was accompanied by increased tumor infiltration of CD8+ T cells. Tumor cell expression of IL1β in vivo was driven by microbial-dependent activation of toll-like receptor 4 (TLR4) signaling and subsequent engagement of the NLRP3 inflammasome. Collectively, these findings identify a hitherto unappreciated role for tumor cell–derived IL1β in orchestrating an immune-modulatory program that supports pancreatic tumorigenesis. Significance: These findings identify a new modality for immune evasion in PDA that depends on IL1β production by tumor cells through TLR4-NLRP3 inflammasome activation. Targeting this axis might provide an effective PDA therapeutic strategy.
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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 | 0.000 |
| 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".