GEO series

A Unique System of Paired PDX Models to Investigate the Progression of Potentially Lethal to Lethal Prostate Tumors at a Multi-omic Level

GSE293911 Homo sapiens (human) Single-cell RNA-seq 2 samples Submitted 2026/04/05 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 2 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism: Homo sapiens (human)
Topic: Prostate cancerThis series is part of our curated briefing on it.
Samples: 2 samples
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.

Summary (written by the submitters)

Prostate cancer (PCa) exhibits substantial heterogeneity in aggressiveness among patients, with significant lethality in its advanced metastatic stages. Defining the molecular mechanisms underlying rapid disease progression remains a pressing unmet need. Here, we present a comparative analysis of a longitudinally paired patient-derived xenograft model of aggressive PCa progression before and after therapeutic intervention.

Read the full summary ↓

Using multi-omic analysis, we characterized the MDA PCa 177-B (androgen receptor [AR]-negative, basal) and MDA PCa 189-1 (AR-positive, luminal) models, which exhibit distinct transcriptomic profiles yet originate from a shared clonal lineage. Targeted and single-cell DNA sequencing revealed that these models share 71.5% of copy number variations, tracing back to a common ancestral cell population. Targeted bisulfite sequencing of both models revealed epigenetic marks unique for aggressive disease compared to normal/benign prostate. Next, through single-cell RNA sequencing, we identified a subset of cells exhibiting a low differentiation profile, present in both PDX models and characterized by a unique molecular signature associated with adaptive immune responses and metastasis-related pathways. Further integrative analysis using data from a broad spectrum of disease samples demonstrated that, within this pair of aggressive PDX models, these low-differentiation clusters are exclusive to stem-like tumor cell populations. These findings support the hypothesis that a continuum of epigenetic, transcriptomic, and phenotypic transitions occurs during the progression of PCa from its potentially lethal to lethal stages. We suggest that this continuum can be captured through well-characterized, longitudinal PDX models derived from the same patient. Our multi-omic approach underscores the value of such models in uncovering the dynamic and adaptive mechanisms of PCa aggressiveness.

This dataset

Open GSE293911 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 GSE293911_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1247410 and SRA study SRP576833. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

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+ 2 more — browse all 2 samples with per-sample file links →

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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.