Work

Construction and Application of Patient-derived Organoid Models for Gastrointestinal Cancer Precision Therapy

Year: 2025
Type: article
Abstract: Gastrointestinal (GI) cancers remain a leading cause of cancer-related mortality worldwide, driven by substantial genetic and phenotypic heterogeneity that limits the efficacy of standard therapies.Three-dimensional organoid systems have emerged as versatile preclinical tools because they preserve patient-specific tumor architecture and molecular signatures while enabling rapid in vitro expansion.This review focuses on three complementary strategies: patientderived organoids (PDOs) that faithfully recapitulate patient-specific heterogeneity; induced pluripotent stem cell (iPSC)-derived organoids enabling controlled gene editing and long-term lineage tracing; and cancer stem-like cell (CSC) organoids from reprogrammed somatic cells modeling early tumor evolution and drug resistance.We compare their relative strengths in high-throughput drug screening, multi-omics biomarker discovery, and artificial intelligence (AI)-based prediction of therapeutic responses.Emerging approaches seek to more accurately recapitulate the tumor microenvironment by incorporating immune and endothelial elements, as well as microfluidic perfusion systems.When integrated with PDO, iPSC, and CSC platforms, these advanced models further enhance the evaluation of immunotherapies and combination regimens under physiologically relevant conditions.Collectively, the convergent application of these platforms offers significant potential for accelerating patient-tailored treatment selection in GI oncology.Supported by omics-based analytics and AI, these organoid models also accelerate biomarker-driven drug development.This review provides a strategic framework for selecting and integrating organoid-based platforms in GI cancer research, aiming to bridge the gap between preclinical modeling and precision oncology and advance translational applications.
Institution: Dong-A University
Language: English
Subfield: Oncology
Field: Medicine
Open Access status: hybrid
Award: G536877067
OpenAlex ID: W4413766945
FWCI:
Cites: 82
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Related to: 10