Spheroid-Based 3D Models to Decode Cell Function and Matrix Effectors in Breast Cancer

Background/Objectives: Conventional two-dimensional (2D) cell cultures offer valuable insights into cancer cell biology; however, they lack in replicating the complex interactions present in solid tumors. Therefore, research has shifted towards the development of three-dimensional (3D) cell models that recapitulate the dynamic cell–cell and cell–matrix interactions within the complex tumor microenvironment (TME), better resembling tumor growth and initial stages of dissemination. Extracellular matrix, a key component within the TME, regulates cell morphology and signaling, influencing key functional properties. Breast cancer remains the most frequently diagnosed cancer type in women and a leading cause of cancer-related mortality. Methods: The aim of the present study was the development of breast cancer cell-derived spheroids, utilizing two breast cancer cell lines with differential estrogen receptor (ER) expression profile, and their characterization in terms of morphology, functional properties, and expression of epithelial-to-mesenchymal transition (EMT) markers and matrix signatures implicated in breast cancer progression. To this end, the ERα-positive MCF-7, and ERβ-positive MDA-MB-231 breast cancer cell lines were utilized. Results: Our findings revealed notable phenotypic transitions between 2D and 3D cultures, which were further supported by differential EMT markers expression. Moreover, spheroids exhibited distinct expression profiles of key receptors [ERs, epidermal growth factor receptor (EGFR) and insulin-like growth factor receptor (IGF1R)] and matrix molecules (syndecans, and matrix metalloproteinases), accompanied by altered functional cell properties. Bioinformatic tools further emphasized the interplay between the studied matrix regulators and their prognostic relevance in breast cancer. Conclusions: Overall, this study introduces a simple yet informative 3D breast cancer model that captures key TME features to better predict cell behavior in vitro.