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Desbois, Mélanie; Udyavar, Akshata R; Ryner, Lisa; Kozlowski, Cleopatra; Guan, Yinghui; Dürrbaum, Milena; Lu, Shan; Fortin, Jean-Philippe; Koeppen, Hartmut; Ziai, James; Chang, Ching-Wei; Keerthivasan, Shilpa; Plante, Marie; Bourgon, Richard; Bais, Carlos; Hegde, Priti; Daemen, Anneleen; Turley, Shannon; Wang, Yulei
Nature communications, 11/2020, Letnik: 11, Številka: 1Journal Article
Close proximity between cytotoxic T lymphocytes and tumour cells is required for effective immunotherapy. However, what controls the spatial distribution of T cells in the tumour microenvironment is not well understood. Here we couple digital pathology and transcriptome analysis on a large ovarian tumour cohort and 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 TGFβ and activated stroma. Furthermore, we identify TGFβ as an important mediator of T cell exclusion. TGFβ reduces MHC-I expression in ovarian cancer cells in vitro. TGFβ also activates fibroblasts and induces extracellular matrix production as a potential physical barrier to hinder T cell infiltration. Our findings indicate that targeting TGFβ might be a promising strategy to overcome T cell exclusion and improve clinical benefits of cancer immunotherapy.
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JCR | SNIP | JCR | SNIP | JCR | SNIP | JCR | SNIP |
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Povezave do osebnih bibliografij avtorjev | Povezave do podatkov o raziskovalcih v sistemu SICRIS |
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Vir: Osebne bibliografije
in: SICRIS
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