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Dissecting the cellular architecture of breast cancer brain metastases reveals prognostically distinct immune landscapes [RNA-Seq]

GSE322943 Homo sapiens (human) Single-nucleus RNA-seq 100 samples Submitted 2026/03/10 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 100 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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)
Samples
100 samples: 75 × NCHGroups 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
Dissecting the cellular architecture of breast cancer brain metastases reveals prognostically distinct immune landscapesCancer cell · 2026
Summary (written by the submitters)
Breast cancer brain metastases (BCBM) are a severe condition with high demand for improved personalized treatment, but a comprehensive understanding of BCBM immune-microenvironment heterogeneity and susceptibility to immunotherapy is lacking. Here, we multimodally profile the immune niche in a vast, clinically well-annotated cohort of 156 BCBM applying tissue cytometry, bulk and single nuclei RNA-sequencing, flow cytometry, and spatial transcriptomics, complemented by functional studies in patient-derived models.
Read the full summary ↓
Integrative analyses reveal two immune landscapes predicting prolonged patient survival and not deducible from paired primary tumors: 1) BCBM with a high proportion of CD8 tissue-resident-like memory T cells as major players of tumor immune control. 2) BCBM containing tertiary lymphoid structures. Surrogate signatures of these landscapes are prognostic in independent BCBM and primary breast cancer cohorts, are associated with fewer metastases, and predict immunotherapy response. Our work provides critical insights into anti-tumor immunity in BCBM and identifies novel biomarkers with translational relevance.
Published in
Dissecting the cellular architecture of breast cancer brain metastases reveals prognostically distinct immune landscapes
Jassowicz L, Feng F, Warta R et al. · Cancer cell 2026 · PMID 42049023 · doi:10.1016/j.ccell.2026.03.016
This dataset
Open GSE322943 in finder → Find more human RNA-seq datasets →
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 GSE322943_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 PRJNA1431999. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42049023) ↗
Study design
75 × NCH

Supports a between-group comparison across 75 samples.

How we read this: 1 replicated groups read from 100 sample titles; they account for 75 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

+ 60 more — browse all 100 samples with per-sample file links →

Datasets on a similar topic

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.