Gut | Targeting Treg–CAFs interaction to overcome immunotherapy resistance in steatotic liver disease-related hepatocellular carcinoma

This study systematically reveals the key mechanisms of immune metabolic reprogramming and Treg interaction with cancer-associated fibroblasts (CAFs) leading to immunosuppression in steatotic liver disease-related hepatocellular carcinoma (SLD-HCC), by integrating single-cell transcriptomics, spatial transcriptomics, and mass cytometry (CyTOF). Blocking TNFRSF14 enhances anti-PD-1 therapy efficacy, offering novel therapeutic strategies for patients with SLD-HCC.

Literature Overview
This article, titled 'Targeting Treg–fibroblast interaction to enhance immunotherapy in steatotic liver disease-related hepatocellular carcinoma', published in the journal 'Gut', reviews and summarizes the characteristics of the immune microenvironment in steatotic liver disease-related hepatocellular carcinoma (SLD-HCC) and its mechanisms of resistance to immunotherapy. The article uses multi-omics approaches to reveal how Treg and CAF interactions contribute to tumor immune exclusion and immunosuppression, and proposes the TNFSF14–TNFRSF14 axis as a potential therapeutic target, providing theoretical support for developing novel combination immunotherapy strategies.

Background Knowledge
Steatotic liver disease-related hepatocellular carcinoma (SLD-HCC) is a subtype of liver cancer with rapidly increasing incidence in recent years. Its characteristic immune-excluded microenvironment leads to poor efficacy of immune checkpoint inhibitors. This study uses scRNA-seq, CyTOF, and spatial transcriptomics to reveal the Treg–CAFs interaction mechanism in SLD-HCC, and validates the therapeutic effect of targeting TNFRSF14 in mouse models. The research provides critical insights into understanding immunotherapy resistance and developing novel interventions.

Research Methods and Experiments
The research team employed single-cell RNA sequencing (scRNA-seq), mass cytometry (CyTOF), spatial transcriptomics (Visium and CosMx technologies), and multiplex immunofluorescence techniques to systematically analyze the tumor microenvironment in 22 cases of SLD-HCC and 31 non-SLD-HCC samples. Additionally, the therapeutic impact of TNFRSF14 blockade on the tumor microenvironment and anti-PD-1 efficacy was evaluated in a high-fat diet-induced mouse model of HCC.

Key Conclusions and Perspectives

• Treg and cancer-associated fibroblasts (CAFs) in SLD-HCC exhibit significant lipid metabolic reprogramming, enhancing their survival advantage in the fatty microenvironment.
• CyTOF analysis reveals a 'cold' tumor microenvironment in SLD-HCC, with reduced CD8+ T cells and enrichment of Treg and immunosuppressive CD4+ T cells.
• Spatial transcriptomic analysis shows that Treg–CAF aggregates are primarily localized at the tumor margin, suggesting a role in immune exclusion.
• Blocking TNFRSF14 significantly reduces Treg infiltration, enhances CD8+ and memory CD4+ T cell activity, and improves the efficacy of anti-PD-1 therapy.
• The study identifies the TNFSF14–TNFRSF14 axis as a key driver of immunotherapy resistance in SLD-HCC, and targeting this axis can effectively overcome resistance.

Conclusion
This study reveals a unique immune metabolic adaptation and Treg–CAF interaction-mediated immunosuppressive mechanism in steatotic liver disease-related hepatocellular carcinoma. Targeting the TNFSF14–TNFRSF14 signaling pathway effectively enhances antitumor immunity and overcomes immunotherapy resistance. These findings provide a theoretical basis for developing SLD-HCC-specific combination immunotherapy regimens and advancing precision cancer immunotherapy.

AbAtlas

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AbAtlas is a tool for dimensionality reduction and visualization of antibody sequences, capable of mapping antibody sequences into two-dimensional and three-dimensional graphics. This tool utilizes data from the Observed Antibody Space(OAS) database, which includes heavy and light chains from six major species (human, mouse, rat, rhesus monkey, camel, and rabbit), as well as their germline genes. By combining AntiBERTy and UMAP, AbAtlas generates high-quality sequence embeddings and effectively performs dimensionality reduction. Simply input an antibody sequence, and AbAtlas will automatically analyze the sequence and visually display its similarity to antibody chains from different species or various V gene families through graphical representations, allowing for the rapid identification of features in the input sequence.