Targeting heterogeneous tumor microenvironments in pancreatic cancer mouse models of metastasis by TGF-β depletion
Bibliographic record
Abstract
The dual tumor-suppressive and -promoting functions of TGF-β signaling has made its targeting challenging. We examined the effects of TGF-β depletion by AVID200/BMS-986416 (TGF-β-TRAP), a TGF-β ligand trap, on the tumor microenvironment of pancreatic ductal adenocarcinoma (PDAC) murine models with different organ-specific metastasis. Our study demonstrated that TGF-β-TRAP potentiates the efficacy of anti-programmed cell death 1 (anti-PD-1) in a PDAC orthotopic murine model with liver metastasis tropism, significantly reducing liver metastases. We further demonstrated the heterogeneous response of cytotoxic effector T cells to combination TGF-β-TRAP and anti-PD-1 treatment across several tumor models. Single-nuclear RNA sequencing suggested that TGF-β-TRAP modulates cancer-associated fibroblast (CAF) heterogeneity and suppresses neutrophil degranulation and CD4+ T cell response to neutrophil degranulation. Ligand-receptor analysis indicated that TGF-β-TRAP may modulate the CCL5/CCR5 axis as well as costimulatory and checkpoint signaling from CAFs and myeloid cells. Notably, the most highly expressed ligands of CCR5 shifted from the immunosuppressive CCL5 to CCL7 and CCL8, which may mediate the immune agonist activity of CCR5 following TGF-β-TRAP and anti-PD-1 combination treatment. This study suggested that TGF-β depletion modulates CAF heterogeneity and potentially reprograms CAFs and myeloid cells into antitumor immune agonists in PDAC, supporting the validation of such effects in human specimens.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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) | 0.000 | 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 teacher head, 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".