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Hepatology (Baltimore, Md.)

October 23, 2025 · via Europe PMC

Glutamine synthetase loss in β-catenin-mutant hepatocellular carcinoma promotes tumor burden through macrophage metabolic reprogramming.

Delgado ER, Patel P, Tao J, +13 more, Monga SP.

Journal ArticleAbstract only — full text under license

AI summary — for doctors, generated from the abstract only

Loss of glutamine synthetase in beta-catenin-mutant liver tumours shifts glutamine handling to macrophages, creating immunosuppression and a worse tumour burden in mice.

What was studied

Preclinical study combining TCGA survival analysis with mouse HCC models carrying mutant CTNNB1 and Nrf2, with conditional Glul deletion in hepatocytes and/or macrophages, macrophage depletion and spatial transcriptomics.

Key findings

  • CTNNB1-mutated HCC patients with low Glul expression had poor survival
  • GS-deficient tumours drove macrophage Glul upregulation and a shift from immunosurveillance to immunosuppression
  • Clodronate depletion or macrophage Glul deletion reduced tumour burden and improved survival

Clinical relevance

It suggests that GS status within beta-catenin-mutant HCC defines distinct biology and a macrophage metabolic vulnerability.

Who should care

HepatologyMedical GastroenterologySurgical GastroenterologyPediatric Hepatology

Limitations

Genetically engineered mouse models and database survival analysis, without clinical intervention data.

No clinical change; this is mechanistic work that may inform future stratified trials.

This is an AI-generated summary of the published abstract, not a clinical recommendation. It may not reflect the full text or subsequent updates — verify against the original source before acting on it.

Abstract

Background and aims

Activating β-catenin gene ( CTNNB1 ) mutations are seen in 30% of all hepatocellular cancer (HCC). These tumors are a molecularly distinct subclass characterized in the majority of cases by the presence of tumor-wide glutamine synthetase (GS), increased glutamine, mTOR activation, and susceptibility to mTOR inhibitors. Here, we investigate the impact of GS loss from β-catenin-mutated HCCs.

Approach

TCGA was assessed for CTNNB1 -mutated HCCs with differential Glul (encoding GS) expression for survival. Glul was conditionally deleted from hepatocytes and/or macrophages in HCCs co-expressing mutant- CTNNB1 (T41A) and mutant Nrf2 in mice. Macrophage depletion was also performed by Clodranate treatment. Tumors were characterized by histology and single-cell spatial transcriptomics.

Results

CTNNB1 -mutated HCC patients with low Glul showed poor survival. β-Catenin-mutated HCCs lacking GS exhibited aggressive disease due to altered glutamate/glutamine availability, forcing metabolic adaptation through upregulation of macrophage Glul , permitting mTOR activation and susceptibility to mTOR inhibitors, but switching macrophage function from immunosurveillance to immunosuppression. Glul loss from tumors did not interfere with β-catenin-dependent tumor zonation and responsiveness to β-catenin inhibition. Depleting macrophages using clodronate or conditionally deleting Glul from macrophages in GS-deficient, β-catenin-mutant HCCs decreased tumor burden and improved survival.

Conclusions

We demonstrate the unique metabolic dependency of β-catenin-mutated HCCs on GS in tumor cells, which is diverted to macrophages upon GS elimination in tumor cells. This adaptation alters macrophage metabolism and function, leading to compromised immunosurveillance and greater tumor burden. Our study reveals a metabolic dynamic between HCC cells and macrophages with an impact on tumor biology.

DOI

10.1097/hep.0000000000001591

PMID

41129335

Research metadata (titles, abstracts, authors, publication dates) is sourced from Europe PMC, which aggregates content from PubMed/MEDLINE, PubMed Central, and other providers under the U.S. National Library of Medicine (NLM/NCBI). NLM does not claim copyright on abstracts; the original journal publishers or authors may hold rights to the full articles. AI-generated summaries are derived only from the abstract, never the full text, and are always labeled as AI-generated.

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