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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.002 | 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".