Dynamic multifunctional profiling of 3D cell models with an automated microfluidic-based assay system

3D cell models derived from patient tumors are highly translational tools that can recapitulate the complex genetic and molecular compositions of solid cancers and accelerate identification of drug targets and drug testing. However, the complexity of performing assays with such models remains a hurdle for their wider adoption. We present here results of disease modeling using primary tumor-derived 3D cell models with an automated organoid assay system (Pu·MA System). It uses a novel microfluidic flowchip that enables 3D high-content imaging (HCI), in situ supernatant sampling, and sensitive luminescence-based metabolite assays. In the example presented, tumoroids were formed from primary cells isolated from a patient-derived tumor explant, TU-BcX-4IC, that represents metaplastic breast cancer with a triple-negative breast cancer subtype. Assays were done using a Pu·MA System which performed media exchanges, compound treatments, and staining of tumoroids in a tissue culture incubator. Tumoroids were characterized by HCI using an ImageXpress Micro Confocal system with image analysis (MetaXpress software) to determine phenotypic responses. Supernatants were sampled from tumoroids at various timepoints and analyzed for metabolites using luminescence assays (Lactate-Glo and Glutamate-Glo). Multifunctional profiling was done of tumoroids treated with anti-cancer drugs for 48 hours. HCI was used to evaluate drug effects on cell viability and expression of E-cadherin and CD44. Lactate secretion was used to measure tumoroid metabolism as a function of time and drug concentration. The IC50 values measured by viability were higher for paclitaxel ( >5 M), than romidepsin and trametinib ( < 25 nM), consistent with lack of patient response to paclitaxel. From the metabolite measurements, increases in lactate secretion were observed in response to treatments indicating an elevated level of glycolysis. Complex dynamic behavior was observed for the different compounds that potentially can be used to understand patient-specific drug resistances. We have demonstrated methods for multifunctional profiling of drug effects in patient-derived tumoroids using automated microfluidics and HCI to measure viability, adhesion markers, and metabolites. These methods can help provide an in depth understanding of drug sensitivity of individual tumor types, with important implications for the future development of personalized medicine.