Elevated protein lactylation promotes immunosuppressive microenvironment and therapeutic resistance in pancreatic ductal adenocarcinoma
Bibliographic record
Abstract
Metabolic reprogramming shapes the tumor microenvironment (TME) and may lead to immunotherapy resistance in pancreatic ductal adenocarcinoma (PDAC). Elucidating the impact of pancreatic cancer cell metabolism in the TME is essential to therapeutic interventions. "Immune cold" PDAC is characterized by elevated lactate levels resulting from tumor cell metabolism, abundance of protumor macrophages, and reduced cytotoxic T cells in the TME. Analysis of fluorine-18 fluorodeoxyglucose (18F-FDG) uptake in patients showed that increased global protein lactylation in PDAC correlates with worse clinical outcomes in immunotherapy. Inhibition of lactate production in pancreatic tumors via glycolysis or mutant-KRAS inhibition reshaped the TME, thereby increasing their sensitivity to immune checkpoint blockade (ICB) therapy. In pancreatic tumor cells, lactate induces K63 lactylation of endosulfine α (ENSA-K63la), a crucial step that triggers STAT3/CCL2 signaling. Consequently, elevated CCL2 secreted by tumor cells facilitates tumor-associated macrophage (TAM) recruitment to the TME. High levels of lactate also drive transcriptional reprogramming in TAMs via ENSA-STAT3 signaling, promoting an immunosuppressive environment. Targeting ENSA-K63la or CCL2 enhances the efficacy of ICB therapy in murine and humanized pancreatic tumor models. In conclusion, elevated lactylation reshapes the TME and promotes immunotherapy resistance in PDAC. A therapeutic approach targeting ENSA-K63la or CCL2 has shown promise in sensitizing pancreatic cancer immunotherapy.
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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".