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Single-Cell RNA Sequencing Identifies Molecular Biomarkers Predicting Late Progression to CDK4/6 Inhibition in Patients with HR+/HER2- Metastatic Breast Cancer [FFPE]

GSE274141 Homo sapiens (human) Single-cell RNA-seq 54 samples Submitted 2025/02/18 Platform GPL18573
At a glance

Single-cell RNA-seq · human · 54 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)
Samples
54 samples: 5 conditions, mostly with about 26 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
Single-cell RNA sequencing identifies molecular biomarkers predicting late progression to CDK4/6 inhibition in patients with HR+/HER2- metastatic breast cancerMolecular cancer · 2025
Summary (written by the submitters)
This study addresses the critical need for predictive biomarkers in patients with hormone receptor–positive, HER2-negative (HR+/HER2-) metastatic breast cancer (mBC) who have been treated with cyclin-dependent kinase 4/6 inhibitors (CDK4/6is). Using single-cell RNA sequencing, we reveal differential mechanisms of early versus late progression, enhancing our understanding of resistance to CDK4/6is.
Read the full summary ↓
We identify and validate tumor-specific molecular biomarkers and demonstrate that tumor-infiltrating cytotoxic natural killer and CD8+ T cells are predictive of therapeutic response. Additionally, increased immune activity suppression in late progressing tumors suggests that combining CDK4/6is with checkpoint inhibitors could be beneficial against drug resistance. Validation across 2 independent cohorts confirms the robustness and clinical relevance of our findings. These insights pave the way for optimizing therapeutic strategies based on microenvironment-specific changes, offering a personalized and effective approach to managing HR+/HER2- mBC and improving patient outcomes.
Published in
Single-cell RNA sequencing identifies molecular biomarkers predicting late progression to CDK4/6 inhibition in patients with HR+/HER2- metastatic breast cancer
Luo L, Yang P, Mastoraki S et al. · Molecular cancer 2025 · PMID 39955556 · doi:10.1186/s12943-025-02226-9
This dataset
Open GSE274141 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 GSE274141_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 PRJNA1145270 and SRA study SRP524702. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39955556) ↗
Study design
5 conditions, mostly with about 26 replicates each
b2_Sample ×26 b3_Sample ×18 b1_Sample ×5 b5_Sample_R813 ×3 b4_Sample_R849 ×2

Supports a between-group comparison across 54 samples.

How we read this: 5 replicated groups read from 54 sample titles; they account for 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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