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Combined gene expression and digital pathology identifies molecular mediators of T cell exclusion and immune suppression in ovarian cancer

Close proximity between cytotoxic T lymphocytes and tumour cells is required for effective immunotherapy. However, what determines the spatial distribution of T cells in the tumour microenvironment is not well understood. Coupling digital pathology and transcriptome analysis on a large ovarian tumour cohort, we develop a machine learning approach to molecularly classify and characterize tumour-immune phenotypes. Our study identifies two important hallmarks characterizing T cell excluded tumours: 1) loss of antigen presentation on tumour cells and 2) upregulation of TGFb and activated stroma. Furthermore, we identify TGFb as a key mediator of T cell exclusion. TGFb reduces MHC-I expression in ovarian cancer cells in vitro; TGFb also activates fibroblasts and induced extracellular matrix (ECM) production as a potential physical barrier to hinder T cell infiltration. Our findings indicate that targeting TGFb may represent a promising strategy to overcome T cell exclusion and improve clinical benefits of cancer immunotherapy.

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Dataset ID Description Technology Samples
EGAD00001004988 Illumina HiSeq 2500 370
Publications Citations
Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer.
Nat Commun 11: 2020 5583
94
In situ tumour arrays reveal early environmental control of cancer immunity.
Nature 618: 2023 827-833
10
Time-Dependent Changes in Risk of Progression During Use of Bevacizumab for Ovarian Cancer.
JAMA Netw Open 6: 2023 e2326834
6
Integrated immunogenomic analyses of high-grade serous ovarian cancer reveal vulnerability to combination immunotherapy.
Front Immunol 15: 2024 1489235
0