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

GSE303200 Homo sapiens (human) RNA-seq (gene expression) 18 samples Submitted 2025/09/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 18 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Homo sapiens (human)
Topic
Breast cancerThis series is part of our curated briefing on it.
Samples
18 samples: 5 × HBCx14 treated with Cisplatin, residual tumor, vs 5 × HBCx14 treated with Carboplatin, residual tumor, vs 5 × HBCx14 treated with Anthracyclines, residual tumor, vs 3 × HBC14 untreated, chemonaive tumor,Groups 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.
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
This dataset
Open GSE303200 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 GSE303200_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1294333. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 × HBCx14 treated with Cisplatin, residual tumor, vs 5 × HBCx14 treated with Carboplatin, residual tumor, vs 5 × HBCx14 treated with Anthracyclines, residual tumor, vs 3 × HBC14 untreated, chemonaive tumor,

Supports a between-group comparison across 18 samples.

How we read this: 4 replicated groups read from 18 sample titles; they account for 18 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
More Breast cancer datasets

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