Abstract 463: Integrated digital pathology and transcriptome analysis identifies molecular mediators of T cell exclusion in ovarian cancer

Abstract

Abstract Background: Close proximity between cytotoxic T lymphocytes and tumor cells is required for effective immunotherapy. Three tumor-immune (TI) phenotypes, infiltrated, excluded and desert, have been previously described based on the infiltration patterns of CD8+ T cells. However, no quantitative methods exist to define these phenotypes robustly in human solid tumors. Importantly, the molecular features and mechanisms determining these phenotypes are not well understood. Here we report a novel integrated approach to classify and functionally dissect TI phenotypes in human ovarian cancer. Methods: CD8 IHC and RNAseq analysis were performed on 370 ovarian tumors from the ICON7 phase III clinical trial, a front-line trial testing the addition of bevacizumab to chemotherapies. A digital image analysis algorithm was developed to quantify the quantity and spatial distribution of CD8+ T cells. Coupling digital pathology with transcriptome analysis, a random forest machine learning algorithm was applied to identify genes associated with these two metrics using a training set (n=155). A gene expression-based classifier was developed for classifying TI phenotypes and validated using testing sets from ICON7 trial and a vendor collection. Functional characterization of key mediators promoting T cell exclusion were carried out by integrating in situ, in vitro and ex vivo analyses on ovarian tumor tissues, cancer associated fibroblasts (CAFs) and ovarian cancer cell lines. Anti-tumor activity of TGFβ blockade in combination with anti-PD-L1 was evaluated in the mouse BrKras ovarian cancer model in FVB background. Results: Integrating digital pathology and machine learning on large ovarian tumor cohorts, we developed and validated a 157-gene molecular classifier. We show the TI phenotypes are of biological and clinical importance in ovarian cancer. Two hallmarks of T cell exclusion were identified: 1) loss of MHC I on tumor cells and 2) upregulation of TGFβ/stromal activities. We show that MHC I in ovarian cancer cells is likely regulated by epigenetic mechanisms and TGFβ is a key mediator of T cell exclusion. TGFβ reduced MHC I expression in ovarian cancer cells and induced extracellular matrix and immunosuppressive molecules in human primary fibroblasts. Finally, we demonstrated that combining anti-TGFβ and anti-PD-L1 in the BrKras mouse model improved the anti-tumor efficacy and survival. Conclusion: This study provided the first systematic and in-depth characterization of the molecular features and mechanisms underlying the tumor-immune phenotypes in human ovarian cancer. We illuminated a multi-faceted role of TGFβ in mediating crosstalk between tumor cells and CAFs to shape the tumor-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. Citation Format: Melanie Desbois, Akshata Udyavar, Lisa Ryner, Cleopatra Kozlowski, Yinghui Guan, Milena Dürrbaum, Shan Lu, Jean-Philippe Fortin, Hartmut Koeppen, James Ziai, Ching-Wei Chang, Amy Lo, Shilpa Keerthivasan, Marie Plante, Richard Bourgon, Carlos Bais, Priti Hegde, Anneleen Daemen, Shannon Turley, Yulei Wang. Integrated digital pathology and transcriptome analysis identifies molecular mediators of T cell exclusion in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 463.