High-Throughput Imaging Assay for Drug Screening of 3D Prostate Cancer Organoids. (October 2021)

• Record Type: Journal Article
• Authors: Choo, Nicholas; Ramm, Susanne; Luu, Jennii; Winter, Jean M.; Selth, Luke A.; Dwyer, Amy R.; Frydenberg, Mark; Grummet, Jeremy; Sandhu, Shahneen; Hickey, Theresa E.; Tilley, Wayne D.; Taylor, Renea A.; Risbridger, Gail P.; Lawrence, Mitchell G.; Simpson, Kaylene J.
• Is Part Of: SLAS discovery. Volume 26:Number 9(2021)
• Page Start: 1107
• Page End: 1124
• Publication Date: 2021-10
• Subjects: organoids -- prostate cancer -- patient-derived xenograft -- PARP inhibitor -- high-content imaging; Drugs -- Analysis -- Periodicals; Drugs -- Testing -- Periodicals; Biomolecules -- Analysis -- Periodicals; Biomolecules -- Analysis; Drugs -- Analysis; Drugs -- Testing; Drug Evaluation, Preclinical; Molecular Biology -- methods; Periodicals; Periodicals; 615.1
• DOI: 10.1177/24725552211020668
• Languages: English
• ISSNs: 2472-5552

Abstract

New treatments are required for advanced prostate cancer; however, there are fewer preclinical models of prostate cancer than other common tumor types to test candidate therapeutics. One opportunity to increase the scope of preclinical studies is to grow tissue from patient-derived xenografts (PDXs) as organoid cultures. Here we report a scalable pipeline for automated seeding, treatment and an analysis of the drug responses of prostate cancer organoids. We established organoid cultures from 5 PDXs with diverse phenotypes of prostate cancer, including castrate-sensitive and castrate-resistant disease, as well as adenocarcinoma and neuroendocrine pathology. We robotically embedded organoids in Matrigel in 384-well plates and monitored growth via brightfield microscopy before treatment with poly ADP-ribose polymerase inhibitors or a compound library. Independent readouts including metabolic activity and live-cell imaging–based features provided robust measures of organoid growth and complementary ways of assessing drug efficacy. Single organoid analyses enabled in-depth assessment of morphological differences between patients and within organoid populations and revealed that larger organoids had more striking changes in morphology and composition after drug treatment. By increasing the scale and scope of organoid experiments, this automated assay complements other patient-derived models and will expedite preclinical testing of new treatments for prostate cancer.