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SPP1+ macrophages in HR+ breast cancer are associated with tumour-infiltrating lymphocytes.

GSE261774 Homo sapiens (human) Single-cell RNA-seq 21 samples Submitted 2024/10/09 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 21 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
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
21 samples: 21 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
SPP1+ macrophages in HR+ breast cancer are associated with tumor-infiltrating lymphocytesNPJ breast cancer · 2024
Summary (written by the submitters)
Breast cancer, categorised into hormone receptor-positive (HR+), HER2-positive (HER2+), and triple-negative (TNBC) subtypes, exhibits varied outcomes based on the number of tumour-infiltrating lymphocytes (TILs). Increased TIL levels are associated with better outcomes in HER2+ and TNBC, while HR+ individuals with high TIL levels show shorter survival but greater neoadjuvant chemotherapy response.
Read the full summary ↓
To explore the divergent roles of TIL levels across various subtypes and their effect on immune cell composition, we employed single-cell RNA sequencing on 31 patients with breast cancer. HR+ breast cancer with high TIL levels (TIL-high) revealed increased SPP1+ macrophages, increased SPP1 expression in other monocytes/macrophages (mono/macro) subgroups, and enriched pathways associated with extracellular matrix (ECM) remodelling in mono/macro. Moreover, cell–cell interaction analyses revealed enhanced SPP1, MIF, and FN1 signalling in the interaction between SPP1+ macrophages and T-cells in TIL-high HR+ breast cancer. Spatial transcriptomics data highlighted the close proximity of SPP1+ macrophages, CD8+ T-cells, and CD4+ T-cells in TIL-high HR+ breast cancer. Our findings unveil the novel influence of SPP1+ macrophages on T-cells in TIL-high HR+ breast cancer, potentially explaining the poor prognosis and offering insights for targeted interventions.
Published in
SPP1+ macrophages in HR+ breast cancer are associated with tumor-infiltrating lymphocytes
Cha SM, Park JW, Lee YJ et al. · NPJ breast cancer 2024 · PMID 39349495 · doi:10.1038/s41523-024-00695-7
This dataset
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.

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 GSE261774_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1088841 and SRA study SRP495941. Searching any of these in the dataset finder brings you back here.

Study design
21 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 21 sample titles: 21 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
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