Abstract

Most of the 3D breast tumor models used in drug screening studies only comprise tumor cells, keeping out other essential cell players of the tumor microenvironment. Tumor-associated macrophages and fibroblasts are frequently correlated with tumor progression and therapy resistance, and targeting these cells at the tumor site has been appointed as a promising therapeutic strategy. However, the translation of new therapies to the clinic has been hampered by the absence of cellular models that more closely mimic the features of in vivo breast tumor microenvironment. Therefore, the development of innovative 3D models able to provide consistent and predictive responses about the in vivo efficacy of novel therapeutics is still an unmet preclinical need.Herein, we have established an in vitro 3D heterotypic spheroid model including MCF-7 breast tumor cells, human mammary fibroblasts and human macrophages. To establish this model, different cell densities have been combined and characterized through the evaluation of the spheroid size and metabolic activity, as well as histological and immunohistochemistry analysis of the 3D multicellular structures. The final optimized 3D model consisted in a multicellular spheroid seeded at the initial density of 5000 cells and cell ratio of 1:2:1 (MCF-7:monocytes:fibroblasts). Our model recapitulates several features of the breast tumor microenvironment, including the formation of a necrotic core, spatial organization, and extracellular matrix production. Further, it was validated as a platform for drug screening studies, using paclitaxel, a currently approved drug for breast cancer treatment, and Gefitinib, a chemotherapeutic approved for lung cancer and in preclinical evaluation for breast cancer. Generally, the impact on the cell viability of the 3D model was less evident than in 2D model, reinforcing the relevance of such complex 3D models in addressing novel treatment approaches. Overall, the use of a 3D heterotypic spheroid of breast cancer could be a valuable tool to predict the therapeutic effect of new treatments for breast cancer patients, by recapitulating key features of the breast cancer microenvironment.

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