High-throughput organo-on-pillar (high-TOP) array system for three-dimensional ex vivo drug testing. (May 2023) - Jun, Hye Ryeong; Kang, Hyun Ju; Ju, Sung Hun; Kim, Jung Eun; Jeon, Sang Youl; Ku, Bosung; Lee, Jae Jun; Kim, Minsung; Kim, Min Jeong; Choi, Jung-Joo; Noh, Joseph J.; Kim, Hyun-Soo; et al.
Abstract: The development of organoid culture technologies has triggered industrial interest in ex vivo drug test-guided clinical response prediction for precision cancer therapy. The three-dimensional culture encapsulated with basement membrane (BM) components is extremely important in establishing ex vivo organoids and drug sensitivity tests because the BM components confer essential structures resembling tumor histopathology. Although numerous studies have demonstrated three-dimensional culture-based drug screening methods, establishing a large-scale drug-screening platform with matrix-encapsulated tumor cells is challenging because the arrangement of microspots of a matrix–cell droplet onto each well of a microwell plate is inconsistent and difficult to standardize. In addition, relatively low scales and lack of reproducibility discourage the application of three-dimensional organoid-based drug screening data for precision treatment or drug discovery. To overcome these limitations, we manufactured an automated organospotter-integrated high-throughput organo-on-pillar (high-TOP) drug-screening platform. Our system is compatible with various extracellular matrices, including BM extract, Matrigel, collagen, and hydrogel. In addition, it can be readily utilized for high-content analyses by simply exchanging the bottom plates without disrupting the domes. Our system demonstrated considerable robustness, consistency, reproducibility, and biological relevancy in three-dimensional drug sensitivity analyses using Matrigel-encapsulated ovarian cancer cell lines. We also demonstrated proof-of-concept cases representing the clinical feasibility of high-TOP-assisted ex vivo drug tests linked to clinical chemo-response in ovarian cancer patients. In conclusion, our platform provides an automated and standardized method for ex vivo drug-sensitivity-guided clinical response prediction, suggesting effective chemotherapy regimens for patients with cancer.