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PBMC single-cell transcriptome discriminates systemic immune changes associated with metastasis

GSE291055 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2025/03/08 Platform GPL20795
At a glance

Single-cell RNA-seq · human · 10 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
Breast cancerThis series is part of our curated briefing on it.
Samples
10 samples: 5 × oligometastatic < 5 metastases vs 5 × metastatic > 5 metastasesGroups 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
Circulating immune cells exhibit distinct traits linked to metastatic burden in breast cancerBreast cancer research : BCR · 2025
Summary (written by the submitters)
Several single-cell studies have reported transcriptomic, genomic and epigenomic dysregulation in breast cancer tissue. However, the peripheral immune landscape of breast cancer patients remains poorly understood. We report the analysis of the single-cell transcriptomes of more than 110,000 peripheral blood mononuclear cells (PBMCs) of healthy and metastatic breast cancer patients.
Read the full summary ↓
Integrated analysis revealed depleted memory B cells (BMEM) and T cell subpopulations, including transitional CD8 T, CD4 naïve, CD4 ribosome and MAIT cells in the circulating immune landscape of metastatic breast cancer patients. Nevertheless, metastatic breast cancer patients showed marked enrichment of pro-inflammatory NKG7 high monocytes, M1 macrophages, migratory myeloid, T memory stem cells (TSCM), CD4 T effector memory (TEM) and CD4 T central memory (TCM) cells. Further, interrogation of single-cell transcriptomes based on metastatic disease state, i.e. stable vs progressive, revealed distinct compositional and transcriptional changes in PBMCs correlated with the metastatic burden. The antigen inexperienced CD4 naïve T cells and cytotoxic MAIT cells are further depleted in the progressive metastatic stage. Surprisingly, the pro-inflammatory classical monocytes and CD4 T effector memory (TEM) cells are also depleted in the progressive metastatic state, indicating their potential role in stable metastatic disease. Lastly, the receptor-ligand relationships, such as cell-cell contact, ECM-receptor interactions, and cytokine transcription profiles, were tightly associated with metastatic burden. Collectively, our study provides unique molecular insight into the peripheral immune system operating in metastatic breast cancers and identified novel surrogate biomarkers of metastatic disease.
Published in
Circulating immune cells exhibit distinct traits linked to metastatic burden in breast cancer
Mangiola S, Brown R, Zhan C et al. · Breast cancer research : BCR 2025 · PMID 40340807 · doi:10.1186/s13058-025-01982-2
This dataset
Open GSE291055 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 GSE291055_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 PRJNA1231471 and SRA study SRP567838. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40340807) ↗
Study design
5 × oligometastatic < 5 metastases vs 5 × metastatic > 5 metastases

Supports a between-group comparison across 10 samples.

How we read this: 2 replicated groups read from 10 sample titles; they account for 10 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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