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

GSE294532 Homo sapiens (human) RNA-seq (gene expression) 63 samples Submitted 2025/10/16 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 63 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
63 samples: 63 samples, each with a distinct title — no replicated groupsGroups are read from the sample names, not from the paper. Check the list below.
Data you can get
A processed table (series matrix) and supplementary files from NCBI; the raw sequencing reads from ENA. Which of these hold counts depends on the study, so check the file list.
Paper
Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug responseJournal of experimental & clinical cancer research : CR · 2025
Summary (written by the submitters)
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.
Read the full summary ↓
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.
Published in
Multi-layer stratified oncology platform utilizing transcriptomics, prostate cancer organoids, and modeling of drug response
Kang J, Chouvardas P, Maalouf A et al. · Journal of experimental & clinical cancer research : CR 2025 · PMID 41094672 · doi:10.1186/s13046-025-03540-2
This dataset
Open GSE294532 in finder → Find more human RNA-seq datasets → Prostate cancer landscape →
Download · which file do I need?

Direct links to NCBI and ENA, no account and no request form. Pick by what you want to do: take the small processed table to look, the supplementary files to re-analyse, or the original raw data to start over.

1 · Just have a look

Processed table

The series matrix: one table of values per sample as the submitters processed it. The smallest download, and it opens in Excel or R. Start here to see what the data look like.

Series matrix ↓
2 · Re-analyse it

Supplementary files

The count tables and other files the authors uploaded. Take only the samples you need, or the whole study as GSE294532_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓
3 · Start from the raw reads

Raw sequencing reads

The original FASTQ files, straight from the sequencer, held at ENA. Large, and you align them yourself, so only choose this if you need to re-process the study from scratch.

Raw reads on ENA ↗

What the files usually look like: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1250458 and SRA study SRP578421. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41094672) ↗
Study design
63 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 63 sample titles: 63 distinct titles with little repetition. These groups are guessed from the sample names, not taken from the paper, so check them against the sample list below before relying on them.

Samples in this study

+ 23 more — browse all 63 samples with per-sample file links →

More Prostate cancer datasets

Search all human RNA-seq datasets in GEO →

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Metadata from NCBI GEO, cached and refreshed periodically — the NCBI page above is authoritative. Downloads link straight to NCBI/ENA; nothing is proxied through BioTransfer.