IL-1β blockade prevents cardiotoxicity and improves the efficacy of immune checkpoint blockers and chemotherapy against pancreatic cancer in mice with obesity
Bibliographic record
Abstract
BACKGROUND: Immune checkpoint blockers (ICBs) have revolutionized cancer therapy, yet they remain largely ineffective in treating pancreatic ductal adenocarcinoma (PDAC). Moreover, ICBs can cause severe immune-related adverse events (irAEs), including fatal cardiac toxicity. Finally, obesity is a risk factor in PDAC that may differentially modulate ICB efficacy in a malignancy-dependent manner. METHODS: We investigated the mechanisms underlying irAEs induced by dual ICB therapy and sought to identify strategies to mitigate them while improving ICB efficacy in the obese setting. To this end, we used a clinically relevant mouse model that integrated key features of human PDAC: (1) high-fat diet-induced obesity, (2) an orthotopic PDAC, and (3) a therapeutic regimen combining chemotherapy (FOLFIRINOX) with ICBs (α-programmed cell death protein-1 + α-cytotoxic T-lymphocyte associated protein-4 antibodies). RESULTS: Obese mice developed cardiac irAEs and had elevated serum interleukin (IL)-1β levels after chemoimmunotherapy. IL-1β blockade not only prevented myocarditis and reduced cardiac fibrosis but also enhanced the antitumor efficacy of the combination of chemotherapy plus dual ICB therapy and significantly improved the overall survival of PDAC-bearing obese mice. CONCLUSIONS: Our findings provide the rationale and compelling data to test a Food and Drug Administration-approved anti-IL-1β antibody in combination with chemotherapy and dual ICB therapy in patients with pancreatic cancer with obesity.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".