Immunosuppression by monocytic myeloid-derived suppressor cells in patients with pancreatic ductal carcinoma is orchestrated by STAT3
Bibliographic record
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) is a highly devastating disease with an overall 5-year survival rate of less than 8%. New evidence indicates that PDAC cells release pro-inflammatory metabolites that induce a marked alteration of normal hematopoiesis, favoring the expansion and accumulation of myeloid-derived suppressor cells (MDSCs). We report here that PDAC patients show increased levels of both circulating and tumor-infiltrating MDSC-like cells. Methods The frequency of MDSC subsets in the peripheral blood was determined by flow cytometry in three independent cohorts of PDAC patients (total analyzed patients, n = 117). Frequency of circulating MDSCs was correlated with overall survival of PDAC patients. We also analyzed the frequency of tumor-infiltrating MDSC and the immune landscape in fresh biopsies. Purified myeloid cell subsets were tested in vitro for their T-cell suppressive capacity. Results Correlation with clinical data revealed that MDSC frequency was significantly associated with a shorter patients’ overall survival and metastatic disease. However, the immunosuppressive activity of purified MDSCs was detectable only in some patients and mainly limited to the monocytic subset. A transcriptome analysis of the immunosuppressive M-MDSCs highlighted a distinct gene signature in which STAT3 was crucial for monocyte re-programming. Suppressive M-MDSCs can be characterized as circulating STAT3/arginase1-expressing CD14+ cells. Conclusion MDSC analysis aids in defining the immune landscape of PDAC patients for a more appropriate diagnosis, stratification and treatment.
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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".