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Consensus molecular classification of triple-negative breast cancers

GSE225002 Homo sapiens (human) RNA-seq (gene expression) 48 samples Submitted 2026/03/10 Platform GPL21697
At a glance

RNA-seq (gene expression) · human · 48 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
Breast cancerThis series is part of our curated briefing on it.
Samples
48 samples: 48 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
Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtypeBreast cancer (Tokyo, Japan) · 2024
Summary (written by the submitters)
The goal of this project was to establish a consensus molecular classification of triple-negative breast cancers (TNBC), ie. to identify TNBC robust subtypes. In order to optimize this process, we retrieved as much transcriptomic data as possible, measured by Affymetrix® microarrays (n = 886) and RNAseq (n = 699). In this last group, we included 48 new TNBC patients followed in our Cancer Centre.
Read the full summary ↓
To our knowledge, this study is the largest one which aims at subtyping TNBC; it exploits a total of 1585 samples. Pooled data were standardized across different public datasets for both gene expression profiling technologies. Subsequently, clustering analysis revealed four robust subtypes.
Published in
Mesenchymal-like immune-altered is the fourth robust triple-negative breast cancer molecular subtype
Jézéquel P, Lasla H, Gouraud W et al. · Breast cancer (Tokyo, Japan) 2024 · PMID 38777987 · doi:10.1007/s12282-024-01597-z
This dataset
Open GSE225002 in finder → Find more human 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 GSE225002_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 PRJNA933542. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38777987) ↗
Study design
48 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 48 sample titles: 48 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

+ 8 more — browse all 48 samples with per-sample file links →

More Breast cancer datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.