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Updated: Jun 6, 2026

A Hepatocellular Cancer Patient-Derived Organoid Xenograft Model to Investigate Impact of Liver Regeneration on Tumor Growth
Published on: February 2, 2024
Personalized therapeutic platform: organoid models for hepatic malignancies
Ziyan Zhou1, Tingting Ma1, Ning Tang1
1Comprehensive Cancer Centre, Department of Oncology, Nanjing Drum Tower Hospital Clinical College of Nanjing Medical University, 321 Zhongshan Road, Nanjing, 210008, China.
Background:
Patient responses to therapeutic agents in hepatic malignancies are highly variable, yet the field lacks robust in vitro models for predicting individual drug responsiveness. Patient-derived organoids (PDOs) that faithfully recapitulate the biological characteristics of parental tumors offer a promising platform for personalized drug testing.
Methods:
We established PDOs from fresh tumor tissues of 29 patients with hepatic malignancies, achieving a success rate of 79.31% (23/29). Histological staining confirmed high morphological and phenotypic consistency between PDOs and original tumor tissues. Drug sensitivity testing was performed using a panel of standard-of-care therapeutics, and model stability was assessed through serial passaging and cryopreservation. Clinical correlation was evaluated by comparing PDO-predicted sensitivity with observed treatment responses in 16 patients followed prospectively.
Results:
The established PDOs maintained structural and functional fidelity to parental tumors and demonstrated reproducible drug sensitivity profiles across serial passaging and cryopreservation. In a prospective clinical validation, patients whose treatment matched PDO-predicted sensitive agents showed significantly improved objective response rate (ORR: 36.36%, 95% CI: 10.93-69.21) and disease control rate (DCR: 90.91%, 95% CI: 58.72-99.77), compared to those receiving PDO-predicted resistant therapies (ORR: 0%, DCR: 20%).
Conclusion:
This study establishes a robust and clinically validated PDO platform for hepatic malignancies that accurately predicts patient-specific drug responses. The model demonstrates high reproducibility and stability, supporting its utility for guiding personalized therapy selection and improving outcomes in patients with liver cancer.
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