Abstract 2096: Multicellular modeling and identification of protein ligand-mediated and exosome-mediated crosstalk signaling cascades in the heterogeneous ovarian tumor microenvironment

Abstract

Abstract Ovarian cancer is the most lethal gynecologic malignancy in the US. While research efforts have been dedicated to identify disease related genes and mutations in cancer cells, the stroma-tumor interactions in the tumor microenvironment and their roles in disease progression is relatively unexplored. The identification of stroma-tumor crosstalk networks with prognostic value presents a unique opportunity for developing new treatment strategies. To capture such an opportunity, a multicellular computational modeling platform, the Cell-Cell Communication Explorer (CCCExplorer), has been developed to identify novel ligand- and exosome-mediated crosstalk networks among different cell types within tumor microenvironment. With transcriptome profiling data generated from laser microdissected ovarian cancer cells and cancer associated fibroblasts (CAFs) in high-grade serous ovarian caner tissue as inputs, CCCExplorer identified TGF-β–dependent and TGF-β–independent Smad signaling networks as protein ligand-mediated crosstalk signaling cascades activated in CAFs associated with poor patient survival rates. Validation studies by co-culturing ovarian cancer cells with CAFs indicated that activation of Smad signaling in CAFs promoted aggressive phenotypes of ovarian cancer cells while inhibition of Smad signaling in CAFs suppressed ovarian cancer progression both in vitro and in vivo. On the other hand, CCCExplorer used transcriptome data generated from exosomes isolated from CAFs and normal fibroblasts (NFs) as well as from CAF- and NF-derived exosomes treated ovarian cancer cells to compute and predict crosstalk cascades mediated by fibroblast-derived exosomes. Our results showed that CAF-derived exosomal microRNAs and LncRNAs promoted signaling cascade activation and the subsequent increase of chemoresistance, motility and proliferation in ovarian cancer cells. After functional validation of predicted crosstalk cascades, CCCExplorer can further be used to query underlying drug information databases to identify and rank candidate drug agents that could target cancer progression-associated crosstalk between ovarian cancer cells and CAFs. Our findings demonstrate a powerful tool at the multi-cellular level of heterogeneous tumor microenvironment in which a computational platform can be used to understand, model, visualize, predict, and target crosstalk signaling cascades and coarse-grained behavior of heterogeneous tumor microenvironment. We are applying CCCExplorer to uncover activated crosstalk networks among ovarian cancer cells and stromal cells and to discover drug compounds targeting these pathways, potentially leading to faster cures for ovarian cancer after the validation and confirmation using in vitro and in vivo models. Citation Format: Tsz-Lun Yeung, Jianting Sheng, Samuel C. Mok, Stephen T. C. Wong. Multicellular modeling and identification of protein ligand-mediated and exosome-mediated crosstalk signaling cascades in the heterogeneous ovarian tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 2096.