(1372-A) Scalable patient derived 3d colorectal cancer organoids in high throughput applications
Monday, February 5, 2024
12:00 PM – 1:00 PM EST
Location: Exhibit Halls AB
Abstract: Despite promising data in vitro, many oncology drugs fail at the later stages of the drug development pipeline. The use of 3D cell models, such as patient-derived organoids (PDOs), offers a promising solution to this problem. Cells grown in 3D can better mimic cell-cell interactions and the tissue microenvironment. Studies show that patients and their derived organoids respond similarly to drugs, suggesting the value of using PDOs to improve therapeutic outcomes. However, challenges such as assay reproducibility, scalability, and cost have limited the use of PDOs in mainstream drug discovery pipelines.
To address challenges associated with the use of PDOs in large scale applications, we have 1. Developed a semi-automated process for the controlled production of PDOs. The bioreactor maintains an environment that ensures constant delivery of nutrients and growth factors to the culture while preventing the accumulation of toxins. This method results in the large-scale production of assay-ready organoids that are uniform in size and have high viability. 2. Developed automation methods to streamline handling of organoid based assays. 3. Developed image based deep learning model for the analysis and 4. Show the use of high dimensionality approach for organoid profiling.
To demonstrate the utility of these PDOs for high throughput applications, we implemented an end-end, automated workflow starting with assay-ready colorectal cancer (CRC) organoids expanded in a bioreactor. CRC organoids were then established in culture, maintained, and screened in an automation enabled work cell consisting of an incubator, high content imager, liquid handler, and robotic plate handler. CRC PDOs were treated with selected anti-cancer compounds, monitored, and analyzed using deep learning-based image segmentation. For cytotoxicity assessment, a viability assay was carried out using high-content imaging. All features per organoid was exported and analyzed using a cloud-based data analytics platform.
We find that most of the compounds showed cytostatic effects on the CRC organoids while only doxorubicin showed additional cytotoxic effects as shown by viability assay. Doxorubicin treated organoids had the most significant reduction in size, with greater number of dead cells compared to controls. Multi-parametric data mining of the dataset revealed additional hits that were not found in the viability assay suggesting the advantages of leveraging high content data extracted from image-based screens.
Overall, our results show the utility of PDOs in high throughput drug discovery applications using automation with high-content imaging.