TGF-β–driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states

Epithelial ovarian cancer (EOC) remains a lethal epithelial malignancy. Immune-checkpoint inhibitors have entered management for recurrent/metastatic disease; yet durable benefit is confined to a subset, reflecting TGF-β-conditioned stromal barriers and organised T-cell exclusion. In this review we summarise advances from single-cell RNA and ATAC profiling and spatial transcriptomics that resolve fibroblast, tumour and immune programmes linked to TGF-β signalling, and appraise translational opportunities spanning selective pathway modulation, checkpoint combinations and spatial biomarkers. We also discuss enduring challenges-including site-specific heterogeneity across adnexal, omental and peritoneal niches, limited assay standardisation and a scarcity of predictive metrics-that temper implementation. By integrating TGF-β-informed readouts (e.g., INHBA<sup>+</sup> cancer-associated fibroblast burden, periostin/fibronectin indices, MHC-I status and CD8-tumour distances) with PD-1-based regimens and TGF-β-axis agents (ALK5 inhibitors, Activin A neutralisation, NOX4-directed reprogramming), emerging strategies aim to restore antigen presentation, improve lymphocyte access and remodel tumour-stroma interfaces. Our synthesis provides an appraisal of the evolving landscape of TGF-β-informed precision immuno-oncology in ovarian cancer and outlines pragmatic standards and avenues for clinical translation. We hope these insights will assist researchers and clinicians as they endeavour to implement more effective, individualised regimens.

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Publication Details

Journal
Frontiers in Immunology
Published
2025-10-17
DOI
https://doi.org/10.3389/fimmu.2025.1698088
Citations
1
Primary Topic
Single-cell and spatial transcriptomics
Type
article
Field-Weighted Citation Impact
0.81
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article

TGF-β–driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states

Yu Zhou, Jiang He, Wenqi Feng, Hongjian Li et al.
1 citations
Frontiers in Immunology
Single-cell and spatial transcriptomics
0.81
article

TGF-β–driven T-cell exclusion in ovarian cancer: single-cell and spatial transcriptomic views of immune low-response states

Yu Zhou, Jiang He, Wenqi Feng, Hongjian Li, Yongqiang Xu, Jun Tao
article en
1 citations

Abstract

Epithelial ovarian cancer (EOC) remains a lethal epithelial malignancy. Immune-checkpoint inhibitors have entered management for recurrent/metastatic disease; yet durable benefit is confined to a subset, reflecting TGF-β-conditioned stromal barriers and organised T-cell exclusion. In this review we summarise advances from single-cell RNA and ATAC profiling and spatial transcriptomics that resolve fibroblast, tumour and immune programmes linked to TGF-β signalling, and appraise translational opportunities spanning selective pathway modulation, checkpoint combinations and spatial biomarkers. We also discuss enduring challenges-including site-specific heterogeneity across adnexal, omental and peritoneal niches, limited assay standardisation and a scarcity of predictive metrics-that temper implementation. By integrating TGF-β-informed readouts (e.g., INHBA<sup>+</sup> cancer-associated fibroblast burden, periostin/fibronectin indices, MHC-I status and CD8-tumour distances) with PD-1-based regimens and TGF-β-axis agents (ALK5 inhibitors, Activin A neutralisation, NOX4-directed reprogramming), emerging strategies aim to restore antigen presentation, improve lymphocyte access and remodel tumour-stroma interfaces. Our synthesis provides an appraisal of the evolving landscape of TGF-β-informed precision immuno-oncology in ovarian cancer and outlines pragmatic standards and avenues for clinical translation. We hope these insights will assist researchers and clinicians as they endeavour to implement more effective, individualised regimens.

Frontiers in ImmunologyVol. 16
Yibin University (CN), Second People’s Hospital of Yibin (CN)
Openalex Percentile: Top 22%
Single-cell and spatial transcriptomics
0.81
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