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Accès ouvert déclaré 2026 article

Optimized Large-Scale Longitudinal Biorepository of Gastroesophageal Adenocarcinoma Patient-Derived Organoids: High-Fidelity Models for Personalized Treatment to Overcome Resistance

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A major limitation in studying gastroesophageal adenocarcinoma (GEA) has been the lack of reliable models that represent the disease’s complexity. We present lessons learned from a comprehensive large-scale biobanking effort combining traditional sample collection with several in vitro models, including 3-dimensional patient-derived organoids (PDOs), 2-dimensional cancer-associated fibroblasts (CAFs), tumor-infiltrating lymphocytes (TILs), and/or in vivo xenografts. This initiative started in 2018, integrating multiple advanced ex vivo models such as PDOs, patient-derived xenografts (PDXs), and organoids (PDXOs). This unique resource now includes tumor avatars from over 380 consented patients, making it the world’s largest living GEA biobank. We achieved a >90% success rate in creating per-patient models, including 227 tumor-derived and 203 neighboring normal PDOs. These organoids accurately mirror key features of the original tumors, such as their histology (e.g., microsatellite instability), mutations, and drug response across treatment points. Notably, PDOs can predict individual patient responses to chemotherapy within five weeks, underscoring their clinical relevance. Furthermore, high-throughput drug screening on PDO subsets with known genetic landscapes generates personalized chemosensitivity profiles for 22 drugs. Through a process of continued refinement of culture techniques and tumor sampling approach, our large-scale comprehensive collection of GEA avatars represents a unique and valuable preclinical experimental resource for precision oncology.

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DOI retrouvé dans Crossref DOI retrouvé ; titre concordant.

Titre Crossref
Optimized Large-Scale Longitudinal Biorepository of Gastroesophageal Adenocarcinoma Patient-Derived Organoids: High-Fidelity Models for Personalized Treatment to Overcome Resistance
Date Crossref
30/03/2026
Éditeur
MDPI AG
Type
journal-article

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Sujets associés

Cancer Cells and MetastasisCancer Genomics and DiagnosticsSingle-cell and spatial transcriptomics

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