Background: New treatments are required for advanced prostate cancer; however, there are fewer preclinical models of prostate cancer than other tumour types to test candidate therapeutics. One opportunity to increase the scope of preclinical studies is to grow tissue from patient-derived xenografts (PDXs), human cancer samples grown in mice, as organoids - 3D in vitro cultures embedded in Matrigel. Indeed, prostate cancer organoids have been used to test drug libraries or specific therapeutics. However, these assays provide only limited, endpoint information about cell viability. Hence, the advances in the use of prostate cancer organoids for screening new therapies is slow.
Objective: To address these shortcomings, we aimed to establish a scalable pipeline for automated seeding, treatment, and analysis of drug responses of prostate cancer organoids.
Study design and results: We established organoid cultures from five 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 384-well plates, and monitored growth via brightfield microscopy prior to treatment with the PARP inhibitor talazoparib. Using bulk and single-organoid readouts of growth, including metabolic activity and live-cell imaging-based features, we showed that the responses of organoids to talazoparib were consistent with the sensitivity of each tumour in vivo. Single organoid analyses enabled in-depth assessment of morphological and compositional differences between patients and within organoid populations, revealing significant decreases in uniformity (Hoechst texture; p<0.0001) and density (Hoechst intensity; p<0.0001) in discrete subpopulations of PARPi-sensitive organoids.
Significance: In conclusion, we have demonstrated the ability to quantify changes in the growth of heterogeneous 3D cultures to candidate therapies across whole wells or specific subpopulations of organoids. 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.