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Perturbing local steroidogenesis to improve breast cancer immunity

GSE255495 Mus musculus (mouse) Single-cell RNA-seq 20 samples Submitted 2025/02/17 Platform GPL24676Platform GPL24247
At a glance

Single-cell RNA-seq · mouse · 20 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
Mus musculus (mouse)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
20 samples: 6 conditions, mostly with about 5 replicates eachGroups 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
Perturbing local steroidogenesis to improve breast cancer immunityNature communications · 2025
Summary (written by the submitters)
Breast cancer, particularly triple-negative breast cancer (TNBC), evades the body's immune defences, in part by fostering an immunosuppressive tumour microenvironment. We present evidence that suppressing local steroidogenesis can enhance anti-tumour immunity against TNBC. Through targeted metabolomics of steroids alongside immunohistochemistry techniques, we initially profiled the presence of various steroids in TNBC patient tumours and identified steroidogenic activity in regions with immune infiltration.
Read the full summary ↓
In mice, genetic inhibition of immune cell steroidogenesis limited TNBC tumour progression, significantly reducing immunosuppressive components such as tumour-associated macrophages, as revealed by single-cell RNA sequencing. Crucially, inhibiting steroidogenesis seems to strengthen anti-tumour immune responses in dendritic cells and T cells by obstructing glucocorticoid signalling, according to RNA sequencing insights. By undertaking metabolic modelling of the single-cell transcriptomics and targeted tumour steroidomics, we identified mast cells and basophils as the primary steroidogenic agents in TNBC, offering pathways for enhanced therapeutic precision.
Published in
Perturbing local steroidogenesis to improve breast cancer immunity
Zhao Q, Pramanik J, Lu Y et al. · Nature communications 2025 · PMID 40287432 · doi:10.1038/s41467-025-59356-3
This dataset
Open GSE255495 in finder → Find more RNA-seq datasets → Breast 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 GSE255495_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 PRJNA1075107 and SRA study SRP489176. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40287432) ↗
Study design
6 conditions, mostly with about 5 replicates each
DCs_Ctrl ×5 DCs_GC ×5 Cyp11a1KO ×3 Ctrl ×3 CD45+mCherry ×2 CD45+mCherry+ ×2

Supports a between-group comparison across 20 samples.

How we read this: 6 replicated groups read from 20 sample titles; they account for 20 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 Breast 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.