Abstract 3093: Combining lab automation with data analysis automation to enable high-throughput screening of patient-derived organoids

Abstract Although patient-derived tumor organoids (PDOs) better-represent patient tumors compared to traditional in vitro cell cultures, limitations of PDO analysis remain a major hurdle for high-throughput screening (HTS) applications. In particular, current PDO analysis fall short in at least 1 of 2 major criteria: 1) accurate recapitulation of patient tumor or 2) scalability. To address this, we have developed an automation-compatible protocol to satisfy these 2 major criteria to facilitate the adoption of PDOs for HTS applications1. Our novel protocol leverages live-cell imaging techniques to capture the dynamic interactions and intricate complexities of PDOs, which allowed for more precise and comprehensive analysis. To further enhance the accuracy and efficiency of our approach, we developed a fully automated image and data analysis pipeline (Orbits). Using label-free detection of organoids from brightfield images, Orbits integrates growth-rate-based drug response metrics with drug synergy metrics to significantly improve the identification of synergistic drug interactions. Here, present the results of our HTS protocol on 10 distinct drug combination strategies, specifically focusing on the repurposed drug candidate Auranofin, a Thioredoxin reductase inhibitor. We screened on 10 PDOs sourced from healthy lung, non-small cell lung cancer, and pancreatic ductal adenocarcinoma. Not only were we able to distinguishing cytostatic from cytotoxic drug responses at a single-organoid level, we identified drug candidates that can synergistically enhanced the efficacy of Auranofin in a tumor selective manner. Our study presents a major step forward in the field of PDO-based drug discovery, offering a robust protocol for HTS and automated data analysis. The implementation of our method will accelerate the discovery of effective therapeutic strategies, ultimately translating to improved patient outcomes. 1 Le Compte, M. , et al JoVE 190 (2022) Citation Format: Abraham Lin, Maxim Le Compte, Tyler Gilcrest, Edgar Cardenas De La Hoz, Geert Roeyen, Jeroen Hendriks, Filip Lardon, Christophe Deben. Combining lab automation with data analysis automation to enable high-throughput screening of patient-derived organoids abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts) ; 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84 (6Suppl): Abstract nr 3093.