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Updated: Aug 7, 2026

Live Imaging to Quantify Cellular Radiosensitivity in Patient-Derived Tumor Organoids
Published on: April 5, 2024
Beyond DNA damage: 3D tumor models and the integrin mechanobiology of radioresistance
Nils Cordes1,2,3,4
1OncoRay-National Center for Radiation Research in Oncology, Faculty of Medicine Carl Gustav Carus, Technische Universität Dresden, PF 41, Fetscherstrasse 74 , 01307, Dresden, Germany. Nils.Cordes@OncoRay.de.
Abstract:
Despite major advances in radiation delivery, clinical outcomes remain constrained by tumor biology rather than technology. Conventional radiobiology has relied on reductionist two-dimensional (2D) systems that fail to capture the spatial, mechanical, and multicellular organization of tumors. Three-dimensional (3D) tumor models now resolve radiation response as a mechanobiological process coordinated across the extracellular matrix (ECM), adhesion signaling, cytoskeleton, and nucleus. This review focuses on a specific and we argue, underappreciated intersection: how 3D models expose integrin-mediated mechanotransduction as a determinant of the DNA damage response (DDR) and therapy resistance epitomized by cell adhesion-mediated radioresistance (CAM-RR) - an organizing principle we frame as the ECM-integrin-nucleus axis. We first delineate which model classes resolve which layer of this biology, distinguishing effects of three-dimensional organization from those of defined ECM-integrin signaling, and we treat the underlying mechanobiology quantitatively rather than descriptively. We then develop a mechanistic framework linking ECM architecture and stiffness to integrin-RTK crosstalk, cytoskeletal tension, LINC-mediated force transfer, and chromatin-dependent DNA repair, in which radiosensitivity emerges as a property of tissue context. From a translational standpoint, 3D models enable functional, radiation-specific assessment of context-dependent radiosensitivity and of mechanically targeted radiosensitization, while their integration with quantitative imaging and computational approaches further supports biomarker-guided and adaptive treatment strategies. We close with testable predictions that this framework generates, intended to guide the next phase of biology-driven radiation oncology.
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