Skip to main page content
Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
, 23 (6), 574-584

Advanced Development of Primary Pancreatic Organoid Tumor Models for High-Throughput Phenotypic Drug Screening


Advanced Development of Primary Pancreatic Organoid Tumor Models for High-Throughput Phenotypic Drug Screening

Shurong Hou et al. SLAS Discov.


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.

Keywords: HTS; cancer; organoid; pancreatic; phenotypic.

Conflict of interest statement

Declaration of Conflicting Interests: The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.


Figure 1.
Figure 1.
A panel of pancreatic cancer-derived cells was evaluated for their ability to form 3D structures using n3D bioprinting technology. (A) The 3D structure formation of primary pancreatic cancer cells (hT1 and hM1), their associated fibroblasts (hT1-CAF and hM1-CAF), and standard cell lines (HT-29 and PANC-1) was monitored using standard microscopy (4× objective) in a Greiner Bio-One 384-well cell-repellent, flat-bottom plate. These cells were also cultured in 384 Corning U-bottom spheroid plates as a point of comparison. (B) Enlarged images of hM1 and hT1 3D culture using a 20× objective representing small organoid-like structures in primary pancreatic cancer culture. (C) A portion of the full 384-well plate image obtained using the Scripps HIAPI instrument is shown to demonstrate that homogeneous hT1-CAF spheroids were formed in each well of a 384-well plate using bioprinting technology.
Figure 2.
Figure 2.
CRCs for five control compounds (oxaliplatin, doxorubicin, gemcitabine, 5-fluorouracil, and SN-38) versus hT1, hT1-CAF, and PANC-1 in 2D and 3D formats (384 and 1536 wells). Each curve represents the mean and standard deviation of four replicates in 384 wells or 16 replicates in 1536 wells.
Figure 3.
Figure 3.
(A) Heat map of the activity of 114 NCI oncology drugs associated with each of the four pancreatic cancer-associated cells in 3D and 2D formats assessed by corresponding log IC50 values (red = increased potency; green = decreased potency). The responses to the most potent drugs, trametinib, romidepsin, bortezomib, carfilzomib, and homoharringtonine, plus gemcitabine, the first-line drug for treating pancreatic cancer, are highlighted below the graph. (B) The correlation plot of the percent inhibition values of the approved drug library tested at 2 µM in the 3D and 2D models of each pancreatic cancer-associated cell.
Figure 4.
Figure 4.
CRCs of (A) carfilzomib, (B) disulfiram, (C) trametinib, and (D) romidepsin tested in the 3D and 2D models of each of the four pancreatic cancer-associated cells. The curve represents the mean and standard deviation in triplicate. IC50 values of those drugs, including other potent inhibitors in each cell model, and the corresponding resistance factor for each cell type are summarized in E.

Similar articles

See all similar articles

Cited by 5 articles


    1. Zanoni M., Piccinini F., Arienti C., et al. 3D Tumor Spheroid Models for In Vitro Therapeutic Screening: A Systematic Approach to Enhance the Biological Relevance of Data Obtained. Sci. Rep. 2016, 6, 19103. - PMC - PubMed
    1. Powell K. Adding Depth to Cell Culture. Science 2017, 356 (6333), 96–98.
    1. Clevers H. Modeling Development and Disease with Organoids. Cell 2016, 165 (7), 1586–1597. - PubMed
    1. Madoux F., Tanner A., Vessels M., et al. A 1536-Well 3D Viability Assay to Assess the Cytotoxic Effect of Drugs on Spheroids. SLAS Discov. 2017, 22 (5), 516–524. - PubMed
    1. Zhang J.-H., Chung T. D. Y., Oldenburg K. R. A Simple Statistical Parameter for Use in Evaluation and Validation of High Throughput Screening Assays. J. Biomol. Screen. 1999, 4 (2), 67–73. - PubMed

Publication types

MeSH terms