Targeting monocyte-intrinsic enhancer reprogramming improves immunotherapy efficacy in hepatocellular carcinoma
Bibliographic record
Abstract
OBJECTIVE: Hepatocellular carcinoma (HCC), mostly developed in fibrotic/cirrhotic liver, exhibits relatively low responsiveness to immune checkpoint blockade (ICB) therapy. As myeloid-derived suppressor cell (MDSC) is pivotal for immunosuppression, we investigated its role and regulation in the fibrotic microenvironment with an aim of developing mechanism-based combination immunotherapy. DESIGN: Functional significance of MDSCs was evaluated by flow cytometry using two orthotopic HCC models in fibrotic liver setting via carbon tetrachloride or high-fat high-carbohydrate diet and verified by clinical specimens. Mechanistic studies were conducted in human hepatic stellate cell (HSC)-peripheral blood mononuclear cell culture systems and fibrotic-HCC patient-derived MDSCs. The efficacy of single or combined therapy with anti-programmed death-1-ligand-1 (anti-PD-L1) and a clinically trialled BET bromodomain inhibitor i-BET762 was determined. RESULTS: Accumulation of monocytic MDSCs (M-MDSCs), but not polymorphonuclear MDSCs, in fibrotic livers significantly correlated with reduced tumour-infiltrating lymphocytes (TILs) and increased tumorigenicity in both mouse models. In human HCCs, the tumour-surrounding fibrotic livers were markedly enriched with M-MDSC, with its surrogate marker CD33 significantly associated with aggressive tumour phenotypes and poor survival rates. Mechanistically, activated HSCs induced monocyte-intrinsic p38 MAPK signalling to trigger enhancer reprogramming for M-MDSC development and immunosuppression. Treatment with p38 MAPK inhibitor abrogated HSC-M-MDSC crosstalk to prevent HCC growth. Concomitant with patient-derived M-MDSC suppression by i-BET762, combined treatment with anti-PD-L1 synergistically enhanced TILs, resulting in tumour eradication and prolonged survival in the fibrotic-HCC mouse model. CONCLUSION: Our results signify how non-tumour-intrinsic properties in the desmoplastic microenvironment can be exploited to reinstate immunosurveillance, providing readily translatable combination strategies to empower HCC 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".