Patient-derived tumor spheroid cultures as a promising tool to assist personalized therapeutic decisions in breast cancer

Breast cancer is the most common cause of cancer related deaths in women. Treatment of breast cancer has many limitations including a lack of accurate biomarkers to predict success of chemotherapy and intrinsic resistance of a significant group of patients to the gold standard of therapy. Therefore, new tools are needed to provide doctors with guidance in choosing the most effective treatment plan for a particular patient and thus to increase the survival rate for breast cancer patients. Here, we present a successful method to grow spheroids from primary breast cancer tissue. Samples were received in accordance with relevant ethical guidelines and regulations. After tissue dissociation, spheroids were generated in a scaffold-free 96-well plate format. Spheroid composition was investigated by immunohistochemistry (IHC) of epithelial [pan cytokeratin (panCK)], stromal (vimentin) and breast cancer-specific markers (ER, PR, HER2, GATA). Growth and cell viability of the spheroids were assessed upon treatment with multiple anti-cancer compounds. Student's -test and two-way ANOVA test were used to determine statistical significance. We were able to successfully grow spheroids from 27 out of 31 samples from surgical resections of breast cancer tissue from previously untreated patients. Recapitulation of the histopathology of the tissue of origin was confirmed. Furthermore, a drug panel of standard first-line chemotherapy drugs used to treat breast cancer was applied to assess the viability of the patient-derived spheroids and revealed variation between samples in the response of the spheroids to different drug treatments. We investigated the feasibility and the utility of an , patient-derived spheroid model for breast cancer therapy, and we conclude that spheroids serve as a highly effective platform to explore cancer therapeutics and personalized treatment efficacy. These results have significant implications for the application of this model in clinical personalized medicine.