The Tautomerase Activity of Tumor Exosomal MIF Promotes Pancreatic Cancer Progression by Modulating MDSC Differentiation
Bibliographic record
Abstract
Pancreatic cancer is a deadly disease that is largely resistant to immunotherapy, in part because of the accumulation of immunosuppressive cells in the tumor microenvironment (TME). Much evidence suggests that tumor-derived exosomes (TDE) contribute to the immunosuppressive activity mediated by myeloid-derived suppressor cells (MDSC) within the pancreatic cancer TME. However, the underlying mechanisms remain elusive. Herein, we report that macrophage migration inhibitory factor (MIF) in TDEs has a key role in inducing MDSC formation in pancreatic cancer. We identified MIF in both human and murine pancreatic cancer-derived exosomes. Upon specific shRNA-mediated knockdown of MIF, the ability of pancreatic cancer-derived exosomes to promote MDSC differentiation was abrogated. This phenotype was rescued by reexpression of the wild-type form of MIF rather than a tautomerase-null mutant or a thiol-protein oxidoreductase-null mutant, indicating that both MIF enzyme activity sites play a role in exosome-induced MDSC formation in pancreatic cancer. RNA sequencing data indicated that MIF tautomerase regulated the expression of genes required for MDSC differentiation, recruitment, and activation. We therefore developed a MIF tautomerase inhibitor, IPG1576. The inhibitor effectively inhibited exosome-induced MDSC differentiation in vitro and reduced tumor growth in an orthotopic pancreatic cancer model, which was associated with decreased numbers of MDSCs and increased infiltration of CD8+ T cells in the TME. Collectively, our findings highlight a pivotal role for MIF in exosome-induced MDSC differentiation in pancreatic cancer and underscore the potential of MIF tautomerase inhibitors to reverse the immunosuppressive pancreatic cancer microenvironment, thereby augmenting anticancer immune responses.
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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".