Acoustic Bioprinting of Patient‐Derived Organoids for Predicting Cancer Therapy Responses. Issue 13 (13th April 2022) - Chen, Hui; Wu, Zhuhao; Gong, Zhiyi; Xia, Yu; Li, Juan; Du, Liang; Zhang, Yuanzheng; Gao, Xiangyang; Fan, Zhou; Hu, Hang; Qian, Qun; Ding, Zhao; et al.
Abstract: Cancer models, which are biologically representative of patient tumors, can predict the treatment responses and help determine the most appropriate cancer treatment for individual patients. Here, a point‐of‐care testing system called acoustically bioprinted patient‐derived microtissues (PDMs) that can model cancer invasion and predict treatment response in individual patients with colorectal cancer (CRC), is reported. The PDMs are composed of patient‐derived colorectal tumors and healthy organoids which can be precisely arranged by acoustic bioprinting approach for recapulating primary tissue's architecture. Particularly, these tumor organoids can be efficiently generated and can apprehend histological, genomic, and phenotypical characteristics of primary tumors. Consequently, these PDMs allow physiologically relevant in vitro drug (5‐fluorouracil) screens, thus predicting the paired patient's responses to chemotherapy. A correlation between organoid invasion speed and normalized spreading speed of the paired patients is further established. It provides a quantitative indicator to help doctors make better decisions on ultimate anus‐preserving operation for extremely low CRC patients. Thus, by combing acoustic bioprinting and organoid cultures, this method may open an avenue to establish complex 3D tissue models for precision and personalized medicine. Abstract : Employing acoustic droplet printing, tumor organoids and colorectal organoids derived from the same patient can be constructed into tumor microtissues. Therapeutic models and invasive ability studies, used to predict drug treatment responses and evaluate intersphincteric resection, maintain high consistency with clinical results and are also extendable for improving adjuvant therapy.