Multifunctional profiling of triple-negative breast cancer patient-derived tumoroids for disease modeling

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. In the present study, we describe methods for processing and multi-functional profiling of tumoroid samples to test compound effects using a novel flowchip system in combination with high content imaging and metabolite analysis. 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 performed in a microfluidics-based device (Pu.MA System) that allows automated exchange of media and treatments of tumoroids in a tissue culture incubator environment. Multi-functional assay profiling was performed on tumoroids treated with anti-cancer drugs. High-content imaging 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. Observed responses included loss of cell viability, decrease in E-cadherin expression, and increase of lactate production. Importantly, the tumoroids were sensitive to romidepsin and trametinib, while showed significantly reduced sensitivity to paclitaxel and cytarabine, consistent with the primary tumor response. These methods for multi-parametric profiling of drug effects in patient-derived tumoroids provide an in depth understanding of drug sensitivity of individual tumor types, with important implications for the future development of personalized medicine.

Automated summary

3D cell models derived from patient tumors can recapitulate the complex genetic and molecular compositions of solid cancers, making them valuable tools for drug target identification and drug testing. However, using these models for assays is still challenging. In this study, the authors describe methods for processing and multi-functional profiling of tumoroids to test compound effects using a novel flowchip system. The results provide an in-depth understanding of the drug sensitivity of individual tumor types, with important implications for personalized medicine.