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Lineage Plasticity Driven by GATA6 Loss Fuels Colorectal Cancer Metastasis [ATAC-seq_met]

GSE290751 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 15 samples Submitted 2026/06/22 Platform GPL19057
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 15 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
15 samples: 5 conditions, mostly in triplicateGroups 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)
Colorectal cancer (CRC) liver metastases are the leading cause of CRC-related mortality, yet the specific genetic and epigenetic drivers that promote this process remain poorly understood. Here, we established a pro-metastatic CRC organoid library through serial orthotopic transplantation of organoids derived from mouse liver metastases.
Read the full summary ↓
Integrative RNA-seq and ATAC-seq analyses of these organoids uncovered distinct transcriptomic and chromatin accessibility profiles correlating with metastatic potential. These pro-metastatic epigenetic changes were characterized by reactivation of fetal-like programs and downregulation of LGR5+ intestinal stem cell signatures. Notably, they converged on the silencing of the transcription factor GATA6, which occurs in liver metastases in both mice and humans. Genetic ablation of GATA6 in mouse models significantly enhanced liver metastasis while having minimal impact on primary tumor growth. Mechanistically, the loss of GATA6 induced the pro-metastatic epigenetic state and promoted the generation of LGR5- cancer cells. Together, these findings demonstrate that GATA6 loss drives liver metastatic progression by reprogramming cancer cells into a pro-metastatic epigenetic state through the hijacking of developmental programs.
This dataset
Open GSE290751 in finder → Find more mouse ChIP / ATAC / CUT&Tag datasets →
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 GSE290751_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 PRJNA1230063 and SRA study SRP567203. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
5 conditions, mostly in triplicate
Met1_AKP ×3 Primary2_AKP ×3 Met2_AKP ×3 Primary3_AKP ×3 Met3_AKP ×3

Supports a between-group comparison across 15 samples.

How we read this: 5 replicated groups read from 15 sample titles; they account for 15 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
Datasets on a similar topic

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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.