From mono- to multi-cellular models: reconstructing colorectal cancer complexity for translational and personalized applications.
1/5 보강
Colorectal cancer (CRC) is a heterogeneous disease shaped by diverse molecular subtypes, tumour microenvironmental context, and rapid adaptation under therapeutic pressure.
APA
Edwards GO, Li Causi FS, et al. (2026). From mono- to multi-cellular models: reconstructing colorectal cancer complexity for translational and personalized applications.. Frontiers in cell and developmental biology, 14, 1777400. https://doi.org/10.3389/fcell.2026.1777400
MLA
Edwards GO, et al.. "From mono- to multi-cellular models: reconstructing colorectal cancer complexity for translational and personalized applications.." Frontiers in cell and developmental biology, vol. 14, 2026, pp. 1777400.
PMID
41918882 ↗
Abstract 한글 요약
Colorectal cancer (CRC) is a heterogeneous disease shaped by diverse molecular subtypes, tumour microenvironmental context, and rapid adaptation under therapeutic pressure. Treatment outcomes remain highly variable, despite advances in screening, molecular profiling, and targeted and immune-based therapies. This is particularly significant in microsatellite-stable disease, where immune exclusion and resistance mechanisms limit therapeutic efficacy. Disease complexity is only partially captured by current biomarkers, contributing to late-stage failure in drug development and suboptimal patient stratification in the clinic. This Mini Review discusses how advanced models (three-dimensional patient-derived organoids, heterotypic co-cultures, organ-on-chip platforms, and tissue models) accurately reflect CRC pathophysiology and support decision-making in both translational research and precision oncology. These approaches preserve key features of CRC (including tumour architecture, clonal diversity, stromal and immune crosstalk, diffusion barriers, and exposure dynamics), facilitating accurate mechanistic studies, rational combination testing, and functional drug-response profiling. Integrating fit-for-purpose systems into translational workflows can help de-risk therapeutic development and support more personalized, effective treatment strategies for CRC patients.
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