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Updated: May 11, 2026
Creating Matched In vivo/In vitro Patient-Derived Model Pairs of PDX and PDX-Derived Organoids for Cancer Pharmacology Research
Published on: May 5, 2021
Ovarian Cancer Patient-Derived Organoids Used as a Model for Replicating Genetic Characteristics and Testing Drug
Yu-Hsun Chang1, Kun-Chi Wu2, Kai-Hung Wang3
1Department of Pediatrics, Hualien Tzu Chi Hospital, Buddhist Tzu Chi Medical Foundation, Tzu Chi University, Hualien.
Abstract:
This study aimed to explore the role of ovarian cancer patient-derived organoids (PDOs) in their replicating genetic characteristics and testing drug responsiveness. Ovarian cancer PDOs were cultured in Matrigel with a specialized medium. The successful rate and proliferation rate were calculated. Morphology, histology, and immunohistochemistry (IHC) (PAX8, P53, and WT1) were used to identify the tumor characteristics. Gene sequencing, variant allele frequency (VAF), and copy number variation were used to explore the mutation profile. The sensitivity to chemodrugs (carboplatin, paclitaxel, gemcitabine, doxorubicin, and olaparib) was conducted. Successful generation of organoids occurred in 54% (7/13) of attempts, encompassing 4 high-grade serous carcinomas (HGSC), 1 mucinous carcinoma (MC), 1 clear cell carcinoma (CCC), and 1 carcinosarcoma. The experiments used six organoids (3 HGSC, 1 CCC, 1 MC, and 1 carcinosarcoma). The derived organoids exhibited spherical-like morphology, and the diameter ranged from 100 to 500 μm. The histology and IHC exhibited the same between organoids and primary tumors. After cryopreservation, the organoid's growth rate was slower than the primary culture (14 days vs 10 days, P < 0.01). Targeted sequencing revealed shared DNA variants, including mutations in key genes, such as BRCA1, PIK3CA, ARID1A, and TP53. VAF was similar between primary tumors and organoids. The organoids maintained inherited most copy number alterations. Drug sensitivity testing revealed varying responses, with carcinosarcoma organoids showing higher sensitivity to paclitaxel and gemcitabine than HGSC organoids. Our preliminary results showed that ovarian cancer PDOs could be successfully derived and histology, mutations, and diverse copy numbers of genotypes could be faithfully captured. Drug testing could reveal the individual PDO's responsiveness to drugs. PDOs might be as valuable resources for investigating genomic biomarkers for personalized treatment.
Insights
Ovarian cancer patient-derived organoids (PDOs) successfully replicate tumor genetics and drug responses. These organoids offer a valuable platform for personalized treatment strategies and biomarker discovery in ovarian cancer.
Area of Science:
- Oncology
- Genetics
- Biotechnology
Background:
- Ovarian cancer patient-derived organoids (PDOs) are emerging models for personalized medicine.
- Understanding their ability to recapitulate primary tumor characteristics is crucial.
Purpose of the Study:
- To evaluate the fidelity of ovarian cancer PDOs in mirroring patient tumor genetics.
- To assess the utility of PDOs for predicting drug responsiveness.
Main Methods:
- PDOs were established from ovarian cancer patient samples.
- Morphological, histological, and immunohistochemical analyses were performed.
- Targeted gene sequencing and copy number variation analysis were conducted.
- Drug sensitivity assays were performed using various chemotherapeutic agents.
Main Results:
- Successful PDO derivation rate was 54%, representing diverse ovarian cancer subtypes.
- PDOs accurately reflected primary tumor histology, mutations (e.g., BRCA1, TP53), and copy number alterations.
- Drug sensitivity profiles varied among PDOs, with some showing distinct responses to specific agents.
- Cryopreserved organoids exhibited slower growth compared to primary cultures.
Conclusions:
- Ovarian cancer PDOs are reliable models for capturing tumor heterogeneity and genetic profiles.
- PDOs demonstrate potential for predicting individual patient drug responses.
- PDOs serve as valuable preclinical tools for personalized ovarian cancer treatment strategies and biomarker identification.
