Abstract 3738: Three-dimensional treatment-resistant breast cancer spheroids as a predictive model of in vivo response to endocrine therapy

Abstract

Abstract 1 out of every 8 U.S. women will develop invasive breast cancer during her lifetime, making it the second most common form of cancer affecting women. Breast cancer is also a leading cause of cancer related deaths for women in the U.S., second only to lung cancer. Activation of estrogen receptor alpha (ERα) is the primary proliferative mechanism of breast cancer cells, making it a logical target for therapy. ER ligands with antiestrogenic activity, such as the selective estrogen receptor modulator (SERM), tamoxifen, and selective estrogen receptor degrader (SERD), fulvestrant, have proven clinically successful as treatments for breast cancer; however, resistance in up to 50% of patients provides a therapeutic challenge. Once resistant, breast cancer cells become endocrine-independent, because of this, there is an urgent need for both novel therapy and improved models of resistant breast cancer. Our lab has created a panel of various endocrine-independent cell lines to mimic SERM and SERD resistance. Along with traditional 2D cell culturing, 3D spheroids have also been utilized to gain a better understanding of resistance. Importantly, the response to therapeutic agents, of cell lines in 2D versus 3D cultures is not identical. We observe that 3D cultures better replicate observations in mouse xenograft models, demonstrating that elements of the spheroid microenvironment, such as cell-cell interactions and the presence of extracellular matrix (EM), mimic aspects of the tumor microenvironment in vivo. Cells cultured as spheroids are therefore a suitable in vitro model for drug discovery, predictive of response in preclinical animal models, in contrast to 2D monolayer cell cultures. Citation Format: Carlo I. Rosales, Jiong Zhao, Lauren M. Gutgesell, Rui Xiong, Debra A. Tonetti, Gregory R. Thatcher. Three-dimensional treatment-resistant breast cancer spheroids as a predictive model of in vivo response to endocrine therapy [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 3738.