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Bioprinted Patient‐Derived Organoid Arrays Capture Intrinsic and Extrinsic Tumor Features for Advanced Personalized Medicine
Author(s) -
Han Jonghyeuk,
Jeong HyeJin,
Choi Jeonghan,
Kim Hyeonseo,
Kwon Taejoon,
Myung Kyungjae,
Park Kyemyung,
Park Jung In,
Sánchez Samuel,
Jung DeokBeom,
Yu Chang Sik,
Song In Ho,
Shim JinHyung,
Myung SeungJae,
Kang HyunWook,
Park TaeEun
Publication year - 2025
Publication title -
advanced science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 5.388
H-Index - 100
ISSN - 2198-3844
DOI - 10.1002/advs.202407871
Subject(s) - organoid , personalized medicine , precision medicine , nanotechnology , computer science , neuroscience , materials science , medicine , psychology , biology , pathology , bioinformatics
Abstract Heterogeneity and the absence of a tumor microenvironment (TME) in traditional patient‐derived organoid (PDO) cultures limit their effectiveness for clinical use. Here, Embedded Bioprinting‐enabled Arrayed PDOs (Eba‐PDOs) featuring uniformly arrayed colorectal cancer (CRC) PDOs within a recreated TME is presented. This model faithfully reproduces critical TME attributes, including elevated matrix stiffness (≈7.5 kPa) and hypoxic conditions found in CRC. Transcriptomic and immunofluorescence microscopy analysis reveal that Eba‐PDOs more accurately represent actual tissues compared to traditional PDOs. Furthermore, Eba‐PDO effectively capture the variability of CEACAM5 expression—a critical CRC marker—across different patients, correlating with patient classification and differential responses to 5‐fluorouracil treatment. This method achieves an uniform size and shape within PDOs from the same patient while preserving distinct morphological features among those from different individuals. These features of Eba‐PDO enable the efficient development of a label‐free, morphology‐based predictive model using supervised learning, enhancing its suitability for clinical applications. Thus, this approach to PDO bioprinting is a promising tool for generating personalized tumor models and advancing precision medicine.

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