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Surface marker identification to capture live circulating tumor cells in metastatic triple negative breast cancer

GSE295462 Homo sapiens (human) Single-cell RNA-seq 47 samples Submitted 2025/12/20 Platform GPL21697
At a glance

Single-cell RNA-seq · human · 47 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
47 samples: 8 conditions, mostly with about 4 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
Surface Marker Identification to Capture Live Circulating Tumor Cells in Metastatic Triple-Negative Breast CancerCancer research communications · 2026
Summary (written by the submitters)
Metastatic triple-negative breast cancer (TNBC) is highly aggressive and lacks targeted therapies. Circulating tumor cells (CTCs) are invaluable for monitoring metastatic tumor progression and treatment response but are difficult to capture due to their rarity and heterogeneity. Surface-based staining for live CTCs is essential to preserve RNA quality in single cells, but current markers tend to perform poorly on more mesenchymal tumor cells such as TNBCs.
Read the full summary ↓
To enhance live TNBC CTC detection, we developed a workflow for live CTC capture and single-cell RNA sequencing (scRNAseq). Using a mouse model of metastatic TNBC, we identified four new CTC surface markers, AHNAK2, CAVIN1, ODR4, and TRIML2, that specifically stain tumor cells. Combining antibodies against these markers improved CTC detection rates in multiple TNBC mouse models and patient samples. Also, combining these new markers with traditional CTC surface markers enhanced detection sensitivity, achieving the highest CTC coverage. This approach identifies diverse CTC populations, while preserving RNA quality for scRNAseq, which is essential for understanding and therapeutically targeting metastatic breast cancer.
Published in
Surface Marker Identification to Capture Live Circulating Tumor Cells in Metastatic Triple-Negative Breast Cancer
Lege BM, Patel KJ, Panici B et al. · Cancer research communications 2026 · PMID 41543394 · doi:10.1158/2767-9764.CRC-25-0536
This dataset
Open GSE295462 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 GSE295462_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 PRJNA1254723 and SRA study SRP580815. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41543394) ↗
Study design
8 conditions, mostly with about 4 replicates each
CTC (LM2) - 5545 ×11 CTC (LM2) - 5546-c ×10 CTC (LM2) - 5546-s ×9 CTC (LM2) - 5549 ×5 Cell culture single cells (LM2) - LM ×4 CTC (LM2) - 5542 ×4 Cell culture single cells (LM2) - LM-P ×2 CTC (LM2) - 5541 ×2

Supports a between-group comparison across 47 samples.

How we read this: 8 replicated groups read from 47 sample titles; they account for 47 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

+ 7 more — browse all 47 samples with per-sample file links →

More Breast cancer datasets

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.