Integrated digital pathology and transcriptome analysis identifies molecular mediators of T-cell exclusion in ovarian cancer
Mélanie Desbois, Akshata R. Udyavar, Lisa Ryner, Cleopatra Kozlowski, Yinghui Guan, Milena Dürrbaum, Shan Lu, Jean‐Philippe Fortin, Hartmut Koeppen, James Ziai, Ching‐Wei Chang, Shilpa Keerthivasan, Marie Plante, Richard Bourgon, Carlos Bais, Priti S. Hegde, Anneleen Daemen, Shannon J. Turley, Yulei Wang
Abstract
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.