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Identification of a shared drug-tolerant persister program in triple-negative breast cancer across treatments and patients [CUT&Tag]

GSE280454 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 23 samples Submitted 2025/09/22 Platform GPL24676
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 23 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
23 samples: 23 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
Characterization of Drug-Tolerant Persister Cells in Triple-Negative Breast Cancer Identifies a Shared Persistence Program across Treatments and PatientsCancer research · 2025
Summary (written by the submitters)
Acquisition of resistance to anti-cancer therapies is a multistep process, which initiates with the survival of drug tolerant persister cells. Understanding the mechanisms driving the emergence of drug tolerant persister cells remains challenging, primarily because of their limited accessibility in patients. Here, using multiple patient-derived models to isolate persister cells, we show these cells are transcriptionally plastic in vivo and return to a common treatment-naïve like state upon relapse, regardless of the treatment they have been exposed to.
Read the full summary ↓
We reveal hallmarks of the persister state in TNBC across treatment modalities: high expression of basal keratins together with activation of a stress response and inflammation pathways. HER2+ breast and lunger cancer cells also activate these hallmarks of drug tolerance in response to targeted therapies. Leveraging gene regulatory networks, we identify AP-1, NFKB and IRF/STAT as the key drivers of this hallmark persister state. As a proof of concept, we show that FOSL1 - an AP-1 member - is sufficient to drive cells to the persister state by binding enhancers and reprogramming the transcriptome of cancer cells. On the contrary, cancer cells without FOSL1 have a decreased ability to reach the persister state. By defining hallmarks of drug persistence to multiple therapies of the standard of care in TNBC, our study provides a resource to design novel combination therapeutic strategies to limit resistance. All datasets produced for this study are available and deposited on GEO at : GSE280454, GSE280455, GSE303200, GSE303201 & GSE303203
Published in
Characterization of Drug-Tolerant Persister Cells in Triple-Negative Breast Cancer Identifies a Shared Persistence Program across Treatments and Patients
Baudre L, Jouault G, Prompsy P et al. · Cancer research 2025 · PMID 41196808 · doi:10.1158/0008-5472.CAN-25-0995
This dataset
Open GSE280454 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE280454_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1178635 and SRA study SRP541607. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41196808) ↗
Study design
23 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 23 sample titles: 23 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
More Breast cancer datasets

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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.