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A single-cell and spatial atlas of prostate cancer reveals the combinatorial nature of gene modules underlying lineage plasticity and metastasis

GSE324131 Homo sapiens (human) Single-cell RNA-seq 38 samples Submitted 2026/05/01 Platform GPL34281
At a glance

Single-cell RNA-seq · human · 38 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism
Homo sapiens (human)
Topic
Prostate cancerThis series is part of our curated briefing on it.
Samples
38 samples: 13 conditions, mostly in duplicateGroups 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
Defining the combinatorial nature of gene modules in prostate cancer underlying lineage plasticity and metastasisResearch square · 2026
Summary (written by the submitters)
Prostate cancer encompasses a spectrum of disease states driven by complex cellular heterogeneity. To delineate the transcriptional programs underlying lineage plasticity and metastasis, we constructed a comprehensive single-cell atlas of 128 patients, spanning localized, castration-resistant, and metastatic disease. Lineage plasticity was prevalent in localized disease, with subsets of tumor cells adopting distinct basal-like and club-like states.
Read the full summary ↓
Luminal-like cancer cells also displayed extensive lineage infidelity, defined not by a binary loss of identity but by the combinatorial erosion of luminal gene modules associated with higher grade and stage. In the metastatic setting, gene program association analysis (GPAS) identified a broad induction of cell-cycle gene modules across organ sites as well as an induction of organ-specific gene modules, including osteomimetic signaling in bone, neuro-migratory genes in brain, and erythroid-like transitions in liver. Neuroendocrine prostate cancers (NEPCs) were not monolithic but defined by combinations of NE-associated gene modules including a novel HES6 program. Notably, these modules were detected at intermediate levels in localized samples, suggesting molecular plasticity precedes histological transformation. We also developed a refined NE signature that could distinguish NEPC tumors more accurately than previously published signatures. Within the tumor microenvironment (TME), we observed an elevation of pro-inflammatory Th17 T-cells in African American patients and identified a rare Schwann cell population. Finally, we present PCformer, a transformer-based foundation model trained on >500,000 cells to automate cell-state classification. Together, this comprehensive atlas demonstrates the complex nature of gene modules underlying lineage infidelity and plasticity in cancer cells and highlights distinct immune and stromal populations within the tumor ecosystem.
Published in
Defining the combinatorial nature of gene modules in prostate cancer underlying lineage plasticity and metastasis
Song H, Xu J, Velazquez-Arcelay K et al. · Research square 2026 · PMID 42183352 · doi:10.21203/rs.3.rs-9430776/v1
This dataset
Open GSE324131 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 GSE324131_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 PRJNA1433496 and SRA study SRP681768. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42183352) ↗
Study design
13 conditions, mostly in duplicate
Patient_PCa3_Tumor_Replica ×4 Patient_PCa6_Tumor_Replica ×4 Patient_PR5316_N_apex_V ×2 Patient_PR5316_N_base_V ×2 Patient_PR5316_T_apex_V ×2 Patient_PROS505-3_replicaA ×2 Patient_PROS505-3_replicaB ×2 Patient_PROS505-3_replicaC ×2 +13 more

Supports a between-group comparison across 30 samples.

How we read this: 13 replicated groups read from 38 sample titles; they account for 30 of them. 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
More Prostate cancer datasets

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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.