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Characterizing heterogeneous cis-regulatory elements in gene regulatory programs associated with breast cancer

GSE299408 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 38 samples Submitted 2025/06/25 Platform GPL21697
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 38 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
38 samples: 38 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
Characterizing heterogeneous cis-regulatory elements in gene regulatory programs associated with breast cancerGenome medicine · 2025
Summary (written by the submitters)
Background: Cis-regulatory elements (CREs) control oncogene expression and malignant phenotypes. The high clinicopathological heterogeneity of cancer cannot be explained by gene expression alone, being attributed to CRE heterogeneity. However, characterizing cancer-associated CRE is challenging. To address this issue, we performed a single-cell epigenomic analysis of clinical specimens.
Read the full summary ↓
Methods: To map the multicellular ecosystem of BC and identify candidate CREs (cCREs), we performed a single-cell assay for transposase-accessible chromatin with sequencing (scATAC-seq) of 38 prospectively collected breast cancer (BC) samples with various clinicopathological characteristics. Results: First, we performed single-cell chromatin accessibility profiling of a high-quality set of 22,775 cells from BC samples. Cells were accurately annotated using marker gene accessibility and integration with existing single-cell RNA sequencing data. By predicting copy number variants, epithelial cells were classified into non-malignant and malignant cells. The chromatin accessibility patterns exhibited by malignant cells were consistent with the clinicopathological features of each tumor. We identified 224,585 cCREs across the BC ecosystem. By identifying cluster-specific differentially accessible cCREs (DA-cCREs) and constructing a putative enhancer-promoter network, we mapped the cis-regulatory landscape for cancer cells and the tumor microenvironment. In this dataset, we identified changes in the cis-regulatory landscape associated with cancer progression. In particular, estrogen receptor (ER)-positive tumors have increased accessibility of ER binding motifs during the cell transition from non-cancerous to cancerous and increased accessibility of Kruppel like factor motifs during the transition from cancerous to metastatic. Furthermore, the accessibility of putative enhancers targeting the same gene differed within or between tumors, highlighting intra- and inter-cis-regulatory tumor heterogeneity. Conclusions: This study provides a valuable resource for future epigenetic research on BC and highlights the diverse regulatory landscape within or among tumor(s), suggesting that cCREs regulate tumor progression and intra- and inter-tumor heterogeneity.
Published in
Characterizing heterogeneous cis-regulatory elements in gene regulatory programs associated with breast cancer
Hori C, Kumegawa K, Saeki S et al. · Genome medicine 2025 · PMID 41299571 · doi:10.1186/s13073-025-01562-1
This dataset
Open GSE299408 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 GSE299408_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 PRJNA1274379. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41299571) ↗
Study design
38 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 38 sample titles: 38 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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