gms | German Medical Science

Objective: Tumor-organoids (TO) are mini-tumors generated from tumor tissue preserving its genotype and phenotype by maintaining the cellular heterogeneity and important components of the tumor microenvironment. We recently developed a protocol to reliably establish TOs from meningioma (MGM) in large quantities. The use of TOs in combination with lab automation holds great promise for drug discovery and screening of comprehensive drug libraries. This might help to tailor patient-specific therapy in the future. Our study aimed to establish an automated drug screening platform utilizing TOs.

Methods: For this purpose, we established TOs by controlled reaggregation of freshly prepared single-cell suspension of MGM tissue samples in the high-throughput format of 384-well plates. The drug screening was performed fully automated by utilizing the robotic liquid handler Hamilton Microlab STAR and a drug library containing 166 FDA-approved oncology agents. First, a two-step screening process was employed: Drugs were screened at a single dose (2.5 µM) for 72h in triplicates. Viability was assessed with CellTiterGlo3D. The top 10 drugs resulting in the lowest viability were then considered for further validation. These drug candidates were then analyzed in a six-point dose-response scheme ranging from 3 nM to 10 µM. Half-maximum inhibitory concentrations (IC50) were calculated from the resulting dose curves. Cluster analyses were performed in the software R.

Results: In total, we performed fully automated drug screening with 166 antineoplastic drugs on TOs from 11 patients suffering from MGM (n=8 WHO°I, n=2 WHO°II, n=1 WHO°III). The top five most effective drugs decreased TO viability, ranging from 84.6–63.3%. K-means clustering analysis resulted in groupings of drugs with similar modes of action. One cluster consisted of epigenetic drugs while another cluster consisted of several proteasome inhibitors. However, when looking at a patient-individual level, in 11 patients 44 of 166 drugs, were among the top 10 most effective drugs, providing strong evidence for heterogeneous drug responses in MGM patients.

Conclusion: Taken together, we successfully developed an automated drug screening platform pipeline utilizing TOs from MGM to identify patient-specific drug responses. The observed intra-individual differences of drug responses mandate for personalized testing of comprehensive drug libraries in TOs to tailor more effective therapies in MGM patients.