Abstract 199: Novel platform for automation of high throughput drug discovery using patient derived colorectal cancer organoids

Abstract

Abstract Most potential oncology drugs fail at the later stages of the drug development pipeline and in clinical trials, despite having promising data for their efficacy in vitro. This high failure rate is partly due to insufficient predictive models being used to screen drug candidates in the early stages of drug discovery. As such, there is a need to develop and utilize more representative models that are amendable for efficient testing of drug efficacy to discover new therapeutic targets. 3D cell models, specifically patient-derived organoids (PDOs), offer a promising solution to this problem. Cells grown in 3D can better mimic cell-cell interactions and the tissue microenvironment, including cancer stem cell niches. Studies show that patients and their derived organoids respond similarly to drugs, suggesting the therapeutic value of using PDOs to improve therapeutic outcomes. However, challenges commonly associated with using these organoids, such as assay reproducibility, ability to scale up, and cost have limited their widespread adoption as a primary screening method in drug discovery. To address some of the hurdles associated with the use of PDOs in large scale screens, a semi-automated bioprocess has been developed for the controlled production of standardized PDOs at scale. PDOs cultured in the bioprocessor were uniform in size, show high viability and were produced in repeatable batches in an assay-ready format. In this study, patient-derived colorectal cancer (CRC) organoids were seeded in high density (96 or 384 well) microtiter plates manually. We also tested the feasibility of scaling up the use of these CRCs by using an automated liquid handler or a bioprinter to seed and culture the PDOs. CRC PDOs were treated with selected anti-cancer compounds at various concentrations. Compound effects were monitored over time using transmitted light imaging. For the analysis of organoid growth and development, a deep learning-based image segmentation model was developed to automate the segmentation of the organoids. Using this approach, we tracked the effects of the compounds on colorectal organoid size, morphology, texture, and additional morphological and phenotypic readouts. A viability assay was carried out using live/dead cell dyes and the PDOs were imaged in 3D on a high-content confocal imager. Out of the tested panel of known anti-cancer drugs, we found that PDOs treated with romidepsin and trametinib showed the most significant reduction in size, with a greater number of dead cells compared to the other compounds and controls. Overall, our results show the potential for the utility of PDOs in both precision medicine and high throughput drug discovery applications, when using automation with high-content imaging. Citation Format: Angeline Lim, Zhisong Tong, Prathyushakrishna Macha, Oksana Sirenkp. Novel platform for automation of high throughput drug discovery using patient derived colorectal cancer organoids [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 199.