High-throughput 3D glioblastoma model for rapid personalized therapeutic screening

Glioblastoma (GBM) is a devastating brain tumor with limited therapeutic success, largely due to the inability to pre-test patient-specific responses to treatment in a reliable and timely manner with diagnostic and analytical methods as current tests fail to provide actionable insights within the critical timeframes necessary for clinical decision-making. To address this gap, we developed a high-throughput 3D culture system utilizing matrix metalloproteinase-responsive starPEG-sulfated glycosaminoglycan hydrogels. This platform enables encapsulation of patient-derived GBM cells, recreating physiologically relevant microenvironments in 384-well plates for rapid, automated drug testing on primary tumor cells from patients. Transcriptomic analyses revealed that 3D cultures significantly resemble primary and recurrent GBM transcriptomic profiles, particularly in pathways critical for tumor growth, invasion, and therapeutic resistance, such as hypoxia, immune modulation, and extracellular matrix regulation. The platform’s versatility extends to drug screening, where single and combinatorial chemotherapeutic treatments elicited robust and reproducible cytoskeletal and transcriptomic responses. Notably, the system effectively characterized synergistic interactions between 5-fluorouracil and carmustine, highlighting its potential for optimizing combinatorial therapies. Our 3D model surpasses traditional 2D cultures in representing tumor-specific molecular programs, offering an invaluable tool for translational research. By delivering reliable, individualized data on therapeutic efficacy within a clinically relevant timeframe, this model holds transformative potential for personalized medicine, enabling optimized, patient-specific treatment strategies for GBM. Its scalability and compatibility with high-throughput workflows position this system as a critical innovation in GBM research, advancing the paradigm of personalized medicine in neuro-oncology.