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Updated: Jan 15, 2026
Prostate Organoid Cultures as Tools to Translate Genotypes and Mutational Profiles to Pharmacological Responses
Published on: October 24, 2019
Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug
Juening Kang1, Panagiotis Chouvardas1,2, Andrew Maalouf1
1Urology Research Laboratory, Department for BioMedical Research, University of Bern, Bern, 3008, Switzerland.
Abstract:
The high intra-patient heterogeneity in multifocal primary prostate cancer (PCa) has curtailed the efficacy of current treatment options. By employing twin biopsies from multiple lesions with matched patient-derived organoids (PDO) models, the PCa molecular heterogeneity was investigated. We utilized genomics, transcriptomics and machine learning (ML) approaches to elucidate and predict the underlying mechanisms of pharmacological heterogeneity. Our data indicate a vulnerability of primary PCa organoids for small molecule inhibitors targeting receptor tyrosine kinases (MET, ALK, SRC). By exploring gene expression data from matched parental tissue in an unsupervised manner, we identified two distinct clusters of samples. Interestingly, the PDO drug responses were significantly different between the two clusters for 4/11 compounds tested. We developed a transcriptomics-based, cluster prediction model, which can accurately stratify samples into the two clusters. Notably, our prediction model is based on tissue profiles, therefore, it can be utilized to rapidly evaluate new cases and suggest promising drug candidates, even when PDO derivation is not feasible. Taken together, we propose a novel flexible stratified oncology approach that can swiftly and accurately highlight promising drug vulnerabilities of PCa patients.
Insights
This study investigated prostate cancer (PCa) heterogeneity using patient-derived organoids (PDOs) and machine learning. Findings reveal drug vulnerabilities and a predictive model for personalized treatment strategies.
Area of Science:
- Oncology
- Genomics
- Pharmacology
Background:
- Multifocal primary prostate cancer (PCa) exhibits high intra-patient heterogeneity, limiting current treatment effectiveness.
- Understanding this heterogeneity is crucial for developing targeted therapies.
Purpose of the Study:
- To investigate PCa molecular heterogeneity and pharmacological responses using patient-derived organoids (PDOs).
- To develop a predictive model for stratifying patients and identifying drug vulnerabilities.
Main Methods:
- Utilized twin biopsies from multiple PCa lesions and matched PDO models.
- Employed genomics, transcriptomics, and machine learning (ML) approaches.
- Analyzed gene expression data to identify patient clusters and predict drug responses.
Main Results:
- Identified distinct patient clusters based on gene expression profiles.
- Demonstrated significant differences in PDO drug responses between clusters for specific compounds targeting receptor tyrosine kinases (MET, ALK, SRC).
- Developed a transcriptomics-based model accurately stratifying samples and predicting cluster membership.
Conclusions:
- Prostate cancer organoids show vulnerability to small molecule inhibitors targeting specific receptor tyrosine kinases.
- A novel, flexible stratified oncology approach can rapidly identify PCa drug vulnerabilities.
- The developed prediction model enables personalized treatment recommendations, even without PDO derivation.
