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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.
Insights
Patient-derived organoids (PDOs) accurately predict liver cancer drug responses, improving treatment selection. This robust model offers a promising tool for personalized medicine in hepatic malignancies.
Area of Science:
- Oncology
- Translational Medicine
- Biotechnology
Background:
- Hepatic malignancies exhibit variable patient responses to therapies.
- Current in vitro models lack robustness for predicting individual drug responsiveness.
- Patient-derived organoids (PDOs) show promise for personalized drug testing due to their ability to recapitulate tumor biology.
Purpose of the Study:
- To establish and validate a patient-derived organoid (PDO) platform for hepatic malignancies.
- To assess the predictive accuracy of PDOs for patient-specific drug responses.
- To evaluate the utility of PDOs in guiding personalized therapy selection for liver cancer.
Main Methods:
- Established PDOs from 29 hepatic malignancy patients with a 79.31% success rate.
- Confirmed PDO-original tumor consistency via histological staining.
- Performed drug sensitivity testing and assessed model stability through serial passaging and cryopreservation.
- Clinically validated PDO predictions against prospective patient treatment outcomes.
Main Results:
- PDOs maintained structural and functional fidelity to parental tumors.
- Reproducible drug sensitivity profiles were observed across serial passaging and cryopreservation.
- Patients receiving PDO-predicted sensitive therapies showed significantly improved objective response rates (36.36%) and disease control rates (90.91%) compared to predicted resistant therapies (ORR: 0%, DCR: 20%).
Conclusions:
- A robust and clinically validated PDO platform for hepatic malignancies was established.
- The PDO model accurately predicts patient-specific drug responses with high reproducibility and stability.
- This platform supports personalized therapy selection, potentially improving outcomes for liver cancer patients.
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