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基于患者来源类器官的声学生物打印用于预测癌症治疗反应。

Acoustic Bioprinting of Patient-Derived Organoids for Predicting Cancer Therapy Responses.

机构信息

Key Laboratory of Artificial Micro- and Nano-structures of Ministry of Education, School of Physics and Technology, Wuhan University, Wuhan, 430072, China.

Department of Colorectal and Anal Surgery, Hubei Key Laboratory of Intestinal and Colorectal Diseases, Zhongnan Hospital of Wuhan University, Wuhan, 430072, China.

出版信息

Adv Healthc Mater. 2022 Jul;11(13):e2102784. doi: 10.1002/adhm.202102784. Epub 2022 Apr 13.

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.

摘要

癌症模型在生物学上能代表患者肿瘤,可以预测治疗反应,并有助于确定针对个体患者的最佳癌症治疗方法。在这里,报告了一种称为声学生物打印患者来源的微组织(PDM)的即时检测系统,它可以模拟结直肠癌(CRC)患者的癌症侵袭,并预测个体患者的治疗反应。PDM 由患者来源的结直肠肿瘤和健康类器官组成,通过声学生物打印方法可以精确排列,以重现原发性组织的结构。特别是,这些肿瘤类器官可以高效生成,并能理解原发性肿瘤的组织学、基因组和表型特征。因此,这些 PDM 允许进行生理相关的体外药物(5-氟尿嘧啶)筛选,从而预测配对患者对化疗的反应。进一步建立了类器官侵袭速度与配对患者归一化扩展速度之间的相关性。它提供了一个定量指标,有助于医生为极低水平的 CRC 患者做出更好的保留肛门手术决策。因此,通过结合声学生物打印和类器官培养,该方法可能为精准和个性化医学开辟建立复杂 3D 组织模型的途径。

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