@article{1186,
  keywords = {Antineoplastic Agents, Cell Line, Tumor, Drug Evaluation, Preclinical, Drug Screening Assays, Antitumor, HT29 Cells, HTS, High-Throughput Screening Assays, Humans, organoids, Pancreatic Neoplasms, Precision Medicine, Cancer, organoid, pancreatic, phenotypic},
  author = {Shurong Hou and Hervé Tiriac and Banu Priya Sridharan and Louis Scampavia and Franck Madoux and Jan Seldin and Glauco R. Souza and Donald Watson and David Tuveson and Timothy P. Spicer},
  title = {Advanced Development of Primary Pancreatic Organoid Tumor Models for High-Throughput Phenotypic Drug Screening},
  abstract = {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.},
  year = {2018},
  journal = {SLAS discovery: advancing life sciences R & D},
  volume = {23},
  pages = {574-584},
  month = {2018-07},
  issn = {2472-5560},
  doi = {10.1177/2472555218766842},
  language = {eng},
}