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Chromatin accessibility and gene expression profiling of primary and metastatic ER+ breast cancer [ATAC-seq]

GSE316389 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 36 samples Submitted 2026/07/15 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 36 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
36 samples: 16 conditions, mostly in duplicateGroups 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)
In this project, we generated a resource dataset that includes ATAC-seq and RNA-seq data from ER positive primary breast tumors and matched liver and lung metastases. We collected samples from four patients at diagnosis in addition to samples from eight patients at autopsy following cancer-related death, allowing us to gain insights into the molecular mechanisms driving metastasis.
Read the full summary ↓
Our research centered on the hypothesis that breast cancer metastasis is driven by tissue-specific enhancer programs, where differential transcription factor motif activity orchestrates distinct gene regulatory networks in metastatic sites. Peaks with divergent TF motif activity between tissues may define key regulatory nodes that control metastatic colonization and tissue adaptation. For ATAC-seq, reads were processed using the PEPATAC pipeline, and DiffBind was applied to identify differentially accessible regions, with ChIPseeker used to annotate metastatic-specific peaks. RNA-seq reads were aligned with STAR and quantified using HTSeq, and DESeq2 was employed to identify differentially expressed genes while controlling for batch effects. We established peak-to-gene correlations within a 0.5 Mbp window between chromatin accessibility and gene expression, assessing significance using a conservative null model. To investigate transcription factor (TF) activity, we applied TOBIAS footprinting, which infers TF occupancy by integrating chromatin accessibility with motif information. Leveraging footprinting scores together with RNA-seq expression, we computed a Total Functional Score of Enhancer Elements to identify the most relevant TFs per tissue. Using this approach, we linked TF motif activity at each peak with gene expression correlations, revealing peaks with tissue-specific TF activity and contrasting motif patterns between Liver and Breast. By connecting ATAC-seq peaks to RNA-seq expression, we identified putative metastasis driver genes, including 99 genes in Liver and 9 in Lung, as well as 23 genes shared across both metastatic sites, such as CCNF, SPINT1, and SLC2A1, which may play critical roles in metastatic progression. Notably, we discovered that the majority of these genes are correlated 3 or more enhancers in the metastatic tissue but not in the primary tissue.
This dataset
Open GSE316389 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 GSE316389_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 PRJNA999001. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in duplicate
A18LIV ×2 A18LUN ×2 A23LIV ×2 A23LUN ×2 A33LIV ×2 A33LUN ×2 A36LIV ×2 A36LUN ×2 +12 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 36 sample titles; they account for 32 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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