GEO series

Single-Cell transcriptomics reveals biomarker heterogeneity linked to CDK4/6 Inhibitor resistance in breast cancer cell lines

GSE298567 Homo sapiens (human) Single-cell RNA-seq 14 samples Submitted 2025/07/28 Platform GPL16791

At a glance

Single-cell RNA-seq · human · 14 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: 14 samples: 14 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: Single-cell transcriptomics reveals biomarker heterogeneity linked to CDK4/6 Inhibitor resistance in breast cancer cell linesNPJ breast cancer · 2025

Summary (written by the submitters)

Cyclin dependent kinases 4 and 6 inhibitors have brought great improvements in the treatment of luminal breast cancer, but resistance is a major clinical hurdle. Multiple biomarkers of resistance have been proposed, but none is currently utilized in clinical practice. By performing single-cell RNA sequencing of seven palbociclib-naïve luminal breast cancer cell lines and palbociclib-resistant derivatives, we show that established biomarkers and pathways related to CDK4/6i resistance present marked intra- and inter- cell-line heterogeneity.

Read the full summary ↓

Transcriptional features of resistance could be already observed in naïve cells correlating with levels of sensitivity (IC50) to palbociclib. Resistant derivatives showed transcriptional clusters that significantly varied for proliferative, estrogen response signatures or MYC targets. This marked heterogeneity was validated in the FELINE trial where, compared to the sensitive ones, ribociclib-resistant tumors developed higher clonal diversity at genetic level and showed greater trascriptional variability for genes associated with resistance . A potential signature of resistance inferred from the cell-line models, positively enriched for MYC targets and negatively enriched for estrogen response markers, was probed on the FELINE trial, separating sensitive from resistant tumors and revealing higher heterogeneity in resistant versus sensitive cells. These data suggest that heterogeneity for CDK4/6 inhibitors resistant markers might facilitate the development of resistance and challenge the validation of clinical biomarkers.

Published in

Single-cell transcriptomics reveals biomarker heterogeneity linked to CDK4/6 Inhibitor resistance in breast cancer cell lines

Migliaccio I, Bonechi M, Romagnoli D et al. · NPJ breast cancer 2025 · PMID 40738893 · doi:10.1038/s41523-025-00803-1

This dataset

Open GSE298567 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 GSE298567_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 PRJNA1269949 and SRA study SRP589007. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40738893) ↗

Study design

14 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 14 sample titles: 14 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

GSM9017402 BT474 cell line, palbociclib resistant
GSM9017403 BT474 cell line, parental
GSM9017404 EDR cell line, palbociclib resistant
GSM9017405 EDR cell line, parental
GSM9017406 MCF7 cell line, palbociclib resistant
GSM9017407 MCF7 cell line, parental
GSM9017408 MDAMB361 cell line, palbociclib resistant
GSM9017409 MDAMB361 cell line, parental
GSM9017410 T47D cell line, palbociclib resistant
GSM9017411 T47D cell line, parental
GSM9017412 TamR cell line, palbociclib resistant
GSM9017413 TamR cell line, parental
GSM9017414 ZR751 cell line, palbociclib resistant
GSM9017415 ZR751 cell line, parental

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