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Targeting FOXA1 and FOXA2 Disrupts the Lineage-Specific Oncogenic Output in Prostate Cancer [ChIP-seq]

GSE281784 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 67 samples Submitted 2025/08/30 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 67 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
Prostate cancerThis series is part of our curated briefing on it.
Samples
67 samples: 20 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)
Activation of the androgen receptor (AR) is the key lineage-specific oncogenic pathway and the primary therapeutic target in prostate cancer. While AR signaling is enabled by the pioneer transcription factor FOXA1, its homologue FOXA2 is specifically expressed in advanced lineage plasticity prostate cancers that have lost the AR signaling axis.
Read the full summary ↓
However, their roles and utility as a drug target remain incompletely characterized. Here, we show an unexpected collaboration of FOXA1 and FOXA2 in mediating AR-independent cell proliferation in different lineage plasticity cancer subtypes. Conversely, joint loss-of-function or pharmacologic disruption of FOXA1 and FOXA2 leads to the collapse of lineage-specific oncogenic transcription factors followed by cell cycle arrest. In summary, our findings uncover a druggable dependency for AR-positive and -negative prostate cancers.
This dataset
Open GSE281784 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Prostate 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 GSE281784_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 PRJNA1185711 and SRA study SRP544991. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 conditions, mostly in duplicate
LNCaP, CTR_INPUT, ×2 LNCaP, CTR_FOXA1, ×2 LNCaP, CTR_H3K27AC, ×2 LNCaP, OE_FOXA2_INPUT, ×2 LNCaP, OE_FOXA2_FOXA1, ×2 LNCaP, OE_FOXA2_H3K27AC, ×2 LNCaP, DMSO_INPUT, ×2 LNCaP, DMSO_FOXA1, ×2 +39 more

Supports a between-group comparison across 40 samples.

How we read this: 20 replicated groups read from 67 sample titles; they account for 40 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

+ 27 more — browse all 67 samples with per-sample file links →

More Prostate cancer datasets

Search all human 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.