GEO series

Identification of a shared drug-tolerant persister program in triple-negactive breast cancer across treatments and patients [scRNA-Seq]

GSE303201 Homo sapiens (human) Single-cell RNA-seq 10 samples Submitted 2025/09/22 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 10 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: 10 samples: 10 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 GSE303201 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 GSE303201_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 PRJNA1294332. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41196808) ↗

Study design

10 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 10 sample titles: 10 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

GSM9120328 HBCx39, chemonaive tumor
GSM9120329 HBCx39 treated with Capecitabine, residual tumor
GSM9120330 HBCx95, chemonaive tumor
GSM9120331 HBCx95 treated with Capecitabine, residual tumor
GSM9120332 HBCx172, chemonaive tumor
GSM9120333 HBCx172 treated with Capecitabine, residual tumor
GSM9120334 HBCx221, chemonaive tumor
GSM9120335 HBCx221 treated with Capecitabine, residual tumor
GSM9120336 HBCx218, chemonaive tumor
GSM9120337 HBCx218 treated with Capecitabine, residual tumor

Datasets on a similar topic

GSE303200 Identification of a shared drug-tolerant persister program in triple-negactive breast cancer across treatments and patients [RNA-Seq] 18 samples shares triple-negactive, drug-tolerant, treatments, persister, program, shared, breast
GSE280454 Identification of a shared drug-tolerant persister program in triple-negative breast cancer across treatments and patients [CUT&Tag] 23 samples shares drug-tolerant, treatments, persister, program, shared, breast
GSE280455 Identification of a shared drug-tolerant persister program in triple-negative breast cancer across treatments and patients [CITE-seq] 5 samples shares drug-tolerant, treatments, persister, program, shared, breast
GSE260499 Focal adhesion kinase-YAP signaling axis drives drug-tolerant persister cells and residual disease in NSCLC (scRNA-Seq) 20 samples shares drug-tolerant, persister, scrna-seq
GSE262597 EIF6 conditions drug-tolerant persister-like transdifferentiation in small cell lung carcinoma 56 samples shares drug-tolerant, persister
GSE254558 Chemotherapy-driven de novo Wnt pathway activation dictates a dynamic shift to a drug-tolerant state in breast cancer cells 46 samples shares drug-tolerant, breast
GSE262593 EIF6 conditions drug-tolerant persister-like transdifferentiation in small cell lung carcinoma (SCLC cell lines) 40 samples shares drug-tolerant, persister
GSE262596 EIF6 conditions drug-tolerant persister-like transdifferentiation in small cell lung carcinoma (eIF6_KD) 16 samples shares drug-tolerant, persister

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.