Brian M. Larsen, Madhavi Kannan, Lee F. Langer, Benjamin D. Leibowitz, Aicha Bentaieb, Andrea Cancino, Igor Dolgalev, Bridgette E. Drummond, Jonathan R. Dry, Chi-Sing Ho, Gaurav Khullar, Benjamin A. Krantz, Brandon Mapes, Kelly E. McKinnon, Jessica Metti, Jason F. Perera, Tim A. Rand, Veronica Sanchez-Freire, Jenna M. Shaxted, Michelle Stein, Michael A. Streit, Yi-Hung Carol Tan, Yilin Zhang, Ende Zhao, Jagadish Venkataraman, Martin C. Stumpe, Jeffrey A. Borgia, Ashiq Masood, Daniel VT Catenacci, Jeremy V. Mathews, Demirkan B. Gursel, Jian-Jun Wei, Theodore H. Welling, Diane M. Simeone, Kevin P. White, Aly A. Khan, Catherine Igartua, Ameen A. Salahudeen
Patient-derived tumor organoids (TOs) are emerging as high-fidelity models to study cancer biology and develop novel precision medicine therapeutics. However, utilizing TOs for systems-biology-based approaches has been limited by a lack of scalable and reproducible methods to develop and profile these models. We describe a robust pan-cancer TO platform with chemically defined media optimized on cultures acquired from over 1,000 patients. Crucially, we demonstrate tumor genetic and transcriptomic concordance utilizing this approach and further optimize defined minimal media for organoid initiation and propagation. Additionally, we demonstrate a neural-network-based high-throughput approach for label-free, light-microscopy-based drug assays capable of predicting patient-specific heterogeneity in drug responses with applicability across solid cancers. The pan-cancer platform, molecular data, and neural-network-based drug assay serve as resources to accelerate the broad implementation of organoid models in precision medicine research and personalized therapeutic profiling programs.
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