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 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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.002 |
| 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".