Abstract
Three-quarters of compounds that enter clinical trials fail to make it to market due to safety or efficacy concerns. This statistic strongly suggests a need for better screening methods that result in improved translatability of compounds during the preclinical testing period. Patient-derived organoids have been touted as a promising 3D preclinical model system to impact the drug discovery pipeline, particularly in oncology. However, assessing drug efficacy in such models poses its own set of challenges, and traditional cell viability readouts fail to leverage some of the advantages that the organoid systems provide. Consequently, phenotypically evaluating complex 3D cell culture models remains difficult due to intra- and inter-patient organoid size differences, cellular heterogeneities, and temporal response dynamics. Here, we present an image-based high-content assay that provides object level information on 3D patient-derived tumor organoids without the need for vital dyes. Leveraging computer vision, we segment and define organoids as independent regions of interest and obtain morphometric and textural information per organoid. By acquiring brightfield images at different timepoints in a robust, non-destructive manner, we can track the dynamic response of individual organoids to various drugs. Furthermore, to simplify the analysis of the resulting large, complex data files, we developed a web-based data visualization tool, the Organoizer, that is available for public use. Our work demonstrates the feasibility and utility of using imaging, computer vision and machine learning to determine the vital status of individual patient-derived organoids without relying upon vital dyes, thus taking advantage of the characteristics offered by this preclinical model system.
Highlights
High-throughput screening assays have advanced the drugdiscovery field by greatly increasing the number of compounds that can be screened and the number of positive leads
We utilized patient-derived organoids (PDOs) from our biobank generated from colorectal cancer (CRC) patient samples, which includes primary and liver metastatic tumors
Supervised machine learning algorithm used to classify organoids as live/dead based on phenotypic features (Figure 1B): Image analysis was subsequently performed on the maximum intensity projections of multiple z-scan images using a machine learning algorithm that enables users to build a linear classifier by identifying regions of interest (ROIs) that are part of distinct groups
Summary
High-throughput screening assays have advanced the drugdiscovery field by greatly increasing the number of compounds that can be screened and the number of positive leads. To improve the success rate of identifying compounds with promising clinical translation, there is a need for more biomimetic preclinical platforms to carry out these drug testing studies. In this context, patient-derived organoids (PDOs), in which cells obtained from a patient’s tumor are grown in a medium that promotes the formation of cellular aggregates that recapitulate important aspects of the original tissue architecture, have gained significant traction in the cancer research field [7,8,9].
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