3D cell culture models have been developed to better mimic the physiological environments that exist in human diseases. As such, these models are advantageous over traditional 2D cultures for screening drug compounds. However, the practicalities of transitioning from 2D to 3D drug treatment studies pose challenges with respect to analysis methods. Patient-derived tumor organoids (PDTOs) possess unique features given their heterogeneity in size, shape, and growth patterns. A detailed assessment of the length scale at which PDTOs should be evaluated (i.e., individual cell or organoid-level analysis) has not been done to our knowledge. Therefore, using dynamic confocal live cell imaging and data analysis methods we examined tumor cell growth rates and drug response behaviors in colorectal cancer (CRC) PDTOs. High-resolution imaging of H2B-GFP-labeled organoids with DRAQ7 vital dye permitted tracking of cellular changes, such as cell birth and death events, in individual organoids. From these same images, we measured morphological features of the 3D objects, including volume, sphericity, and ellipticity. Sphericity and ellipticity were used to evaluate intra- and interpatient tumor organoid heterogeneity. We found a strong correlation between organoid live cell number and volume. Linear growth rate calculations based on volume or live cell counts were used to determine differential responses to therapeutic interventions. We showed that this approach can detect different types of drug effects (cytotoxic vs cytostatic) in PDTO cultures. Overall, our imaging-based quantification workflow results in multiple parameters that can provide patient- and drug-specific information for screening applications.

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.

Organoids are an emerging model system that more closely recapitulates the in vivo environment compared to traditional monolayer systems. Using patient-derived samples grown in a 3D matrix, we hope to advance personalized medicine by providing more patient-specific treatment plans. We have begun building a patient-derived organoid library from primary colon cancer tumors and metastases. Once cells are isolated and seeded into a Basement Membrane Extract (BME) matrix we analyze features on a high content imaging platform, the Operetta (Perkin Elmer). Using the Operetta we are able to obtain quantitative phenotypic data that includes the number, size, and morphology of organoids. In addition, we can capture the dynamics of organoid formation, growth, and death by tracking the same organoids over time using live-cell imaging. More specifically, for each organoid, we capture z-stack images at various heights, and using the Harmony software, we perform quantitative analysis on the maximum projection of the complied images. Using a building block approach, we created a workflow that first relied on identifying regions of interest (ROI), which correspond to organoids. In each ROI we then obtained information such as geometric center, length and width measurements, and texture features. We used this information to determine inter-patient heterogeneity, as well as differences across samples isolated from the primary location (i.e. colon) versus metastatic sites (i.e. liver). After culturing patient-derived organoids for several weeks in laboratory conditions, we perturbed various aspects of the tumor microenvironment (e.g. drug and oxygen levels) and subsequently tracked changes in organoid number, size, and morphology over time. This workflow represents a unique method for image-based quantitative phenotypic analysis of organoids under varying environmental conditions. Citation Format: Erin Spiller, Roy Lau, Sarah Choung, Shannon M. Mumenthaler. High-content 3D image analysis of patient-derived organoids. [abstract]. In: Proceedings of the AACR Special Conference: Patient-Derived Cancer Models: Present and Future Applications from Basic Science to the Clinic; Feb 11-14, 2016; New Orleans, LA. Philadelphia (PA): AACR; Clin Cancer Res 2016;22(16_Suppl):Abstract nr A18.

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