Dynamic profiling of immune microenvironment during pancreatic cancer development suggests early intervention and combination strategy of immunotherapy
Bibliographic record
Abstract
Background Pancreatic ductal adenocarcinoma (PDAC) has little response to immune checkpoint inhibitors. An in-depth understanding of the immune microenvironment from a comprehensive and dynamic perspective is critical to generate effective therapeutic strategies for PDAC. Methods Using mass cytometry and immunohistochemistry, we explored the dynamic changes of tumor-infiltrating immune cells during the development of PDAC in a genetically engineered mouse model (Kras G12D/+ ; Trp53 R172H/+ ; Pdx1-cre) and human specimens. PD-L1 −/− mice were crossed with Kras G12D/+ ; TgfβR2 flox/flox ; Ptf1a-cre mice to achieve early depletion of PD-L1 in pancreatic cancer. Combination therapy of Arginase-1 (Arg-1) inhibitor and anti-PD-1 mAb was validated in syngeneic mouse models. Findings Two different stages of immunosuppression with unique features were observed in both mouse model and human specimens. Early stage of immunosuppression featured highly abundant Tregs during acinar-to-ductal metaplasia, despite of a prominent and continuous presence of effector lymphocytes. The differentiation/activation branch of Ly-6C + monocytes changed from a BST2 + /MHC-II + phenotype to an Arg-1 + phenotype over time during PDAC development. The late stage of immunosuppression thus featured the presence of a large number of myeloid suppressive cells together with a significant reduction of effector lymphocytes. Removal of PD-L1 from the beginning efficiently triggered anti-tumor immunity and significantly prolonged survival in PDAC-developing mice. Targeting Arg1 + macrophages with an Arg-1 inhibitor synergized with anti-PD-1 immunotherapy and led to PDAC-specific immune memory. Interpretation By demonstrating the coevolution of histopathology and immunology in PDAC, this study highlights the necessity and value of early intervention and combinational approach in leveraging immunotherapy to treat pancreatic cancer. Funding A full list of funding bodies that contributed to this study can be found in the Acknowledgements section.
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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 | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".