# Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug response

## TL;DR

This study introduces a new approach to prostate cancer treatment by combining organoid models and transcriptomics to identify drug vulnerabilities and predict responses.

## Contribution

A novel stratified oncology platform using transcriptomics and organoids to predict drug responses and classify prostate cancer samples.

## Key findings

- Primary prostate cancer organoids show vulnerability to small molecule inhibitors targeting MET, ALK, and SRC.
- Two distinct clusters of samples were identified based on gene expression data from matched parental tissue.
- A transcriptomics-based model accurately stratifies samples and predicts drug responses without requiring organoid derivation.

## 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.

The online version contains supplementary material available at 10.1186/s13046-025-03540-2.