Abstract 2267
Background
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 large ovarian tumour cohorts, here we report classification and functionally dissection of tumour-immune contexture in human ovarian cancer.
Methods
CD8 IHC and RNAseq analysis were performed on 370 ovarian tumours from the ICON7 phase III clinical trial. Coupling digital pathology with transcriptome analysis, a random forest machine learning algorithm was developed and independently validated for classifying tumour-immune phenotypes in ovarian cancer. Anti-tumour activity of TGFβ blockade in combination with anti-PD-L1 was evaluated in an ovarian cancer mouse model.
Results
We show the identified tumour-immune phenotypes are of biological and clinical importance with interconnection to molecular subtypes and association with clinical outcome in ovarian cancer. Two important hallmarks of T cell exclusion were identified: 1) loss of antigen presentation on tumour cells and 2) upregulation of TGFβ and activated stroma. We identified TGFβ as a key mediator of T cell exclusion. TGFβ reduced MHC class I expression in ovarian cancer cells and induced extracellular matrix (ECM) production and immunosuppressive molecules in human primary fibroblasts. Finally, we demonstrated that combination of anti-TGFβ and anti-PD-L1 in a mouse ovarian cancer model significantly improved the anti-tumour efficacy and survival.
Conclusions
This study provided the first systematic and in-depth characterization of the molecular features and mechanisms underlying the tumour-immune phenotypes in ovarian cancer. We illuminated a multi-faceted role of TGFβ in mediating consequential crosstalk between tumour cells and cancer associated fibroblasts to shape the tumour-immune contexture. Our findings support that targeting the TGFβ pathway represents a promising therapeutic strategy to overcome T cell exclusion and optimize response to cancer immunotherapy.
Clinical trial identification
Editorial acknowledgement
Legal entity responsible for the study
The authors.
Funding
Genentech/Roche.
Disclosure
All authors have declared no conflicts of interest.
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