Novel Phenotypic Fluorescent Three-Dimensional Co-Culture Platforms for Recapitulating Tumor in vivo Progression and for Personalized Therapy

摘要

Because three-dimensional (3D) in vitro models are more accurate than 2D cell culture models and faster and cheaper than animal models, they have become a prospective trend in the biomedical and pharmaceutical fields, especially for personalized and targeted therapies. Because appropriate 3D models can be customized to mimic the in vivo microenvironment wherein various cell populations grow within an intricate but well organized extracellular matrix (ECM), they can accurately recapitulate physiological and pathophysiological progressions. The majority of cancers are carcinomas, which originate from epithelial cells, and dynamically interact with non-malignant cells including stromal cells (fibroblasts), vascular cells (endothelial cells and pericytes), immune cells (macrophages and mast cells), and the ECM. Employing a tumor monoclonal colony, tumor xenograft or patient cancer biopsy into an in vivo-like microenvironment, the native signaling pathways, cell-cell and cell-matrix interactions, and cell phenotypes are preserved and our fluorescent phenotypic 3D co-culture platforms can then accurately recapitulate the tumor in vivo scenario including tumor induced angiogenesis, tumor growth, and metastasis. In this paper, we describe a robust and standardized method to co-culture a tumor colony or biopsy with different cell populations, e.g., endothelial cells, immune cells, pericytes, etc. The procedures for recovering cells from the co-culture for molecular analyses, imaging, and analyzing are also described. We selected ECM solubilized extract derived from Engelbreth-Holm-Swam sarcoma cells. Because the 3D co-culture platforms can provide drug chemosensitivity data within 9 days that is equivalent to the results generated from mouse tumor xenograft models in 50 days, the 3D co-culture platforms are more accurate, efficient, and cost-effective and may replace animal models in the near future to predict drug efficacy, personalize therapies, prevent drug resistance, and improve the quality of life.