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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.

Click on a Dataset ID in the table below to learn more, and to find out who to contact about access to these data

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
In situ tumour arrays reveal early environmental control of cancer immunity.
Nature 618: 2023 827-833