Abstract IA15: Modeling the tumor microenvironment of high-grade serous ovarian cancer

Abstract

Abstract Using biopsies of metastatic high-grade serous ovarian cancer (HGSOC) that ranged from minimal to extensive disease, we defined RNA and protein profiles that evolved with changes in cellularity, architecture, and tissue modulus. This gave new insights into host response to cancer as well as leukocyte, cytokine, and matrisome regulation in the tumor microenvironment (TME). Although we had studied a single metastatic site, we identified an extracellular matrix (ECM) gene expression signature, which we named the matrix index, that significantly associated with increased stiffness and disease score. High matrix index distinguished patients with a shorter overall survival in ovarian and twelve other primary cancers, suggesting a common matrix response to human cancer. We used this “deconstruction” analysis of a human TME to build 3D multicellular cultures of human HGSOC cells, primary omental fibroblasts, mesothelial cells, and adipocytes. These cultures reproduce the prognostic matrix index signature, as confirmed by RNAseq and immunofluorescence, of the human cancer biopsies. Using these 3D models, we can study regulation of the matrisome and cancer cell invasion. Similarly, we have conducted multilevel analysis of orthotopic mouse models of HGSOC metastases with relevant oncogenic mutations. This analysis identified significant correlations between the transcriptome, host cell infiltrates, immune response, matrisome, vasculature, and tissue modulus of mouse and human TMEs, with several stromal and malignant cell targets in common. However, each mouse model showed distinct differences and potential vulnerabilities that enabled us to predict response to chemotherapy and an anti-IL-6 antibody. The transcriptional profiles of the mouse tumors that differed in chemotherapy response were able to classify chemotherapy-sensitive and -refractory patient tumors. We believe that these 3D human and mouse models provide useful preclinical tools and may help identify subgroups of HGSOC patients most likely to respond to specific therapies. Citation Format: Frances Balkwill. Modeling the tumor microenvironment of high-grade serous ovarian cancer [abstract]. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr IA15.