1 The Scripps Research Institute Molecular Screening Center, Department of Molecular Medicine, Scripps Florida, Jupiter, FL, USA.
These authors contributed equally to this work.
SLAS Discov. 2018 Jul;23(6):574-584. doi: 10.1177/2472555218766842. Epub 2018 Apr 19.
Traditional high-throughput drug screening in oncology routinely relies on two-dimensional (2D) cell models, which inadequately recapitulate the physiologic context of cancer. Three-dimensional (3D) cell models are thought to better mimic the complexity of in vivo tumors. Numerous methods to culture 3D organoids have been described, but most are nonhomogeneous and expensive, and hence impractical for high-throughput screening (HTS) purposes. Here we describe an HTS-compatible method that enables the consistent production of organoids in standard flat-bottom 384- and 1536-well plates by combining the use of a cell-repellent surface with a bioprinting technology incorporating magnetic force. We validated this homogeneous process by evaluating the effects of well-characterized anticancer agents against four patient-derived pancreatic cancer KRAS mutant-associated primary cells, including cancer-associated fibroblasts. This technology was tested for its compatibility with HTS automation by completing a cytotoxicity pilot screen of ~3300 approved drugs. To highlight the benefits of the 3D format, we performed this pilot screen in parallel in both the 2D and 3D assays. These data indicate that this technique can be readily applied to support large-scale drug screening relying on clinically relevant, ex vivo 3D tumor models directly harvested from patients, an important milestone toward personalized medicine.
传统的肿瘤高通量药物筛选通常依赖于二维(2D)细胞模型,该模型不能充分重现癌症的生理环境。三维(3D)细胞模型被认为能更好地模拟体内肿瘤的复杂性。已经描述了许多培养 3D 类器官的方法,但大多数方法都不均匀且昂贵,因此不适合高通量筛选(HTS)目的。在这里,我们描述了一种 HTS 兼容的方法,该方法通过将细胞排斥表面与结合磁力的生物打印技术结合使用,可在标准平底 384 孔和 1536 孔板中一致地生成类器官。我们通过评估针对四种源自患者的胰腺癌细胞 KRAS 突变相关原代细胞(包括癌相关成纤维细胞)的经过充分表征的抗癌药物的作用,验证了这种均一的过程。通过完成对约 3300 种已批准药物的细胞毒性初步筛选,测试了该技术与 HTS 自动化的兼容性。为了突出 3D 格式的优势,我们在 2D 和 3D 测定中同时进行了该初步筛选。这些数据表明,该技术可直接从患者中直接采集到的临床相关的体外 3D 肿瘤模型,轻松应用于支持大规模药物筛选,这是迈向个性化医疗的重要里程碑。