Beyond the Monolayer: Leveraging
3D
Culture Systems to Decode Mechanisms of Drug Resistance and Therapeutic Vulnerabilities in Colorectal Cancer
Sarah J. Harmych, Bhuminder Singh ABSTRACT
Two‐dimensional (2D) monolayer cultures remain the workhorse of colorectal cancer (CRC) research and high‐throughput screening (HTS), yet they poorly recapitulate tissue architecture, extracellular matrix (ECM) cues, metabolic gradients, and multicellular heterogeneity that govern drug response in vivo. Over the past decade, three‐dimensional (3D) culture systems—including spheroids, patient‐derived organoids (PDOs), tissue‐originated spheroids (CTOS), and collagen‐embedded models—have revealed striking 2D vs. 3D discordance in proliferation, signaling, epithelial‐mesenchymal transition (EMT), plasticity, and sensitivity to standard chemo‐ and targeted therapies. This review synthesizes how 3D CRC models are used to decode mechanisms of primary and acquired resistance to anti‐CRC therapies, from metabolic phenotypes and ECM remodeling to EMT, polarity loss, and rare invasive trajectories identified by imaging and quantification of morphologic features of CRC cells grown in 3D. Parallel advances in PDO‐based functional precision oncology demonstrate that organoid drug screens can prospectively predict patient response in advanced CRC, while 3D combination screens uncover format‐specific synergies that are invisible in 2D. Building on this foundation, we highlight recent technical innovations and discuss how integrating morphometric, metabolic, and trajectory‐level readouts can move CRC drug discovery “beyond live/dead.” Rather than propose a single best model, we outline practical guidelines for matching 3D platform choice (cell lines vs. PDO/CTOS, collagen vs. Matrigel, static vs. live‐imaging HTS) to mechanistic questions and therapeutic goals in CRC. We propose an integrated roadmap—matrix‐embedded 3D cultures, high‐content morphological endpoints, trajectory analytics, and rigorous HTS validation—to decode resistance mechanisms and prioritize clinically actionable combinations in CRC.