Institute for Biomedical Materials and Devices, School of Mathematical and Physical Sciences, University of Technology Sydney, Broadway Ultimo, Sydney, New South Wales, 2007, Australia.
St. Vincent's Clinical School, Faculty of Medicine, University of New South Wales Sydney, Darlinghurst, New South Wales, 2010, Australia.
Adv Sci (Weinh). 2021 Nov;8(21):e2102418. doi: 10.1002/advs.202102418. Epub 2021 Sep 8.
Mammary tumor organoids have become a promising in vitro model for drug screening and personalized medicine. However, the dependency on the basement membrane extract (BME) as the growth matrices limits their comprehensive application. In this work, mouse mammary tumor organoids are established by encapsulating tumor pieces in non-adhesive alginate. High-throughput generation of organoids in alginate microbeads is achieved utilizing microfluidic droplet technology. Tumor pieces within the alginate microbeads developed both luminal- and solid-like structures and displayed a high similarity to the original fresh tumor in cellular phenotypes and lineages. The mechanical forces of the luminal organoids in the alginate capsules are analyzed with the theory of the thick-wall pressure vessel (TWPV) model. The luminal pressure of the organoids increase with the lumen growth and can reach 2 kPa after two weeks' culture. Finally, the mammary tumor organoids are treated with doxorubicin and latrunculin A to evaluate their application as a drug screening platform. It is found that the drug response is related to the luminal size and pressures of organoids. This high-throughput culture for mammary tumor organoids may present a promising tool for preclinical drug target validation and personalized medicine.
乳腺肿瘤类器官已成为药物筛选和个性化医疗的有前途的体外模型。然而,对基底膜提取物 (BME) 作为生长基质的依赖限制了它们的综合应用。在这项工作中,通过将肿瘤块包裹在非粘附性藻酸盐中来建立乳腺肿瘤类器官。利用微流控液滴技术在藻酸盐微珠中实现了高通量的类器官生成。藻酸盐微珠内的肿瘤块形成了腔型和实体型结构,并且在细胞表型和谱系上与原始新鲜肿瘤高度相似。用厚壁压力容器 (TWPV) 模型理论分析了藻酸盐胶囊中腔型类器官的力学。随着腔的生长,类器官的腔内压力增加,培养两周后可达到 2 kPa。最后,用阿霉素和拉曲库铵处理乳腺肿瘤类器官,以评估其作为药物筛选平台的应用。结果发现,药物反应与类器官的腔大小和压力有关。这种高通量的乳腺肿瘤类器官培养方法可能为临床前药物靶点验证和个性化医疗提供一种有前途的工具。