← BioTransfer GEO Dataset Finder
GEO series

EZH2 directs breast cancer progression through the modulation of epithelial cellular plasticity [ChIP-Seq]

GSE303502 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2025/10/27 Platform GPL34328
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 16 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
Mus musculus (mouse)
Samples
16 samples: 16 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
EZH2 directs HER2+ breast cancer progression through the modulation of epithelial plasticityEMBO reports · 2026
Summary (written by the submitters)
Breast cancer remains a leading cause of death among women, encompassing diverse molecular subtypes with distinct clinical outcomes. The HER2+ subtype is particularly aggressive, with many patients eventually developing resistance to HER2-targeted therapies, underscoring the urgent need for alternative treatment strategies. Enhancer of Zeste Homolog 2 (EZH2), the catalytic subunit of Polycomb Repressive Complex 2, represses the expression of genetic programs crucial for differentiation, control of proliferation, and apoptosis through histone H3 lysine 27 trimethylation (H3K27me3).
Read the full summary ↓
To investigate the role of EZH2 in HER2+ tumor progression, we crossed a genetically engineered mouse model (GEMM) of HER2-driven breast cancer with a conditional Ezh2 knockout strain. The results showed that Ezh2 is essential for accelerating tumor initiation and metastatic dissemination in HER2-driven breast cancer. Combined bulk and single cell RNA sequencing analyses revealed a significant downregulation of basal cell populations in the absence of Ezh2, characterized by the loss of Tp63 expression, and an upregulation of luminal progenitor cell populations, driven by crucial transcription factors such as Esr1, thereby driving luminal lineage commitment. Further, inhibition of EZH2 in vitro resulted in increased expression of ER in HER2+ human breast cancer cell lines and rendered them susceptible to tamoxifen. Altogether, these findings demonstrate that EZH2 dictates breast cancer plasticity through control of cellular identity and provides rationale for combining EZH2 inhibitors with endocrine therapies to improve outcomes in HER2+ breast cancer.
Published in
EZH2 directs HER2+ breast cancer progression through the modulation of epithelial plasticity
Liu L, Massey EJ, Zuo D et al. · EMBO reports 2026 · PMID 41606266 · doi:10.1038/s44319-026-00691-x
This dataset
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.

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 GSE303502_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1295532 and SRA study SRP603138. Searching any of these in the dataset finder brings you back here.

Study design
16 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 16 sample titles: 16 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
Datasets on a similar topic

Search all mouse ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.