PLAUR <sup>+</sup> Neutrophils Drive Anti‐PD‐1 Therapy Resistance in Patients with Hepatocellular Carcinoma by Shaping an Immunosuppressive Microenvironment
Bibliographic record
Abstract
Abstract Hepatocellular carcinoma (HCC) is characterized by an immunosuppressive tumor microenvironment (TME) that limits the efficacy of immune checkpoint inhibitors. However, the role of distinct neutrophil subsets within the TME in mediating tumor resistance to immunotherapy remains poorly understood. Here, the present study reveals that the urokinase‐type plasminogen activator receptor (PLAUR) + neutrophils are enriched in immunotherapy non‐responders and correlate with poor prognosis. Through multi‐omics analyses of clinical cohorts and preclinical models, PLAUR + neutrophils identify as a pivotal driver of immunotherapy resistance by shaping an immunosuppressive TME. Mechanistically, spatial transcriptomics and single‐cell RNA sequencing reveal that PLAUR + neutrophils orchestrate immune evasion by CD8 + T cell exclusion and macrophage‐dependent immune suppression. Through the structure‐based virtual screening, a novel PLAUR inhibitor is identified that could reverse the immunosuppressive phenotype of neutrophils. In various in vivo tumor models, PLAUR inhibitor suppresses tumor growth and potentiates the efficacy of anti‐programmed cell death protein 1 (PD‐1) therapy. These results demonstrate that PLAUR + neutrophils serve as a critical regulator of immunotherapy resistance and targeting PLAUR is a promising strategy to augment the efficacy of anti‐PD‐1 therapy in HCC.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".