GEO series

Metastatic site influences driver gene function in pancreatic cancer [ATAC-seq]

GSE245821 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 16 samples Submitted 2025/05/14 Platform GPL21103

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: 6 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.
Paper: SMAD4 induces opposite effects on metastatic growth from pancreatic tumors depending on the organ of residenceNature cancer · 2025

Summary (written by the submitters)

Driver gene mutations can increase the metastatic potential of the primary tumor, but their role in sustaining tumor growth at metastatic sites is poorly understood. A paradigm of such mutations is inactivation of SMAD4 – a transcriptional effector of TGFβ signaling – which is a hallmark of multiple gastrointestinal malignancies. SMAD4 inactivation mediates TGFβ’s remarkable anti- to pro-tumorigenic switch during cancer progression and can thus influence both tumor initiation and metastasis.

Read the full summary ↓

To determine whether metastatic tumors remain dependent on SMAD4 inactivation, we developed a mouse model of pancreatic ductal adenocarcinoma (PDAC) that enables Smad4 depletion in the pre-malignant pancreas and subsequent Smad4 reactivation in established metastases. As expected, Smad4 inactivation facilitated the formation of primary tumors that eventually colonized the liver and lungs. By contrast, Smad4 reactivation in metastatic disease had opposite effects depending on the tumor’s organ of residence: suppression of liver metastases and promotion of lung metastases. Integrative multiomic analysis revealed organ-specific differences in the tumor cells’ epigenomic state, whereby the liver and lungs harbored chromatin programs respectively dominated by the KLF and RUNX developmental transcription factors, with Klf4 depletion being sufficient to reverse Smad4’s tumor-suppressive activity in liver metastases. Our results show how epigenetic states favored by the organ of residence can influence the function of driver genes in metastatic tumors. This organ-specific gene–chromatin interplay invites consideration of anatomical site in the interpretation of tumor genetics, with implications for the therapeutic targeting of metastatic disease.

Published in

SMAD4 induces opposite effects on metastatic growth from pancreatic tumors depending on the organ of residence

Tsanov KM, Barriga FM, Ho YJ et al. · Nature cancer 2025 · PMID 40999053 · doi:10.1038/s43018-025-01047-5

This dataset

Open GSE245821 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 GSE245821_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 PRJNA1030029 and SRA study SRP467418. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40999053) ↗

Study design

6 conditions, mostly in triplicate

Panc-Smad4OFF_m ×3 Panc-Smad4ON_m ×3 Lung-Smad4OFF_m ×3 Lung-Smad4ON_m ×3 Liv-Smad4OFF_m ×2 Liv-Smad4ON_m ×2

Supports a between-group comparison across 16 samples.

How we read this: 6 replicated groups read from 16 sample titles; they account for 16 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

GSM7849476 Panc-Smad4OFF_m1 Panc-Smad4OFF_m
GSM7849477 Panc-Smad4OFF_m2 Panc-Smad4OFF_m
GSM7849478 Panc-Smad4OFF_m3 Panc-Smad4OFF_m
GSM7849479 Panc-Smad4ON_m1 Panc-Smad4ON_m
GSM7849480 Panc-Smad4ON_m2 Panc-Smad4ON_m
GSM7849481 Panc-Smad4ON_m3 Panc-Smad4ON_m
GSM7849482 Liv-Smad4OFF_m1 Liv-Smad4OFF_m
GSM7849483 Liv-Smad4OFF_m2 Liv-Smad4OFF_m
GSM7849484 Liv-Smad4ON_m1 Liv-Smad4ON_m
GSM7849485 Liv-Smad4ON_m2 Liv-Smad4ON_m
GSM7849486 Lung-Smad4OFF_m1 Lung-Smad4OFF_m
GSM7849487 Lung-Smad4OFF_m2 Lung-Smad4OFF_m
GSM7849488 Lung-Smad4OFF_m3 Lung-Smad4OFF_m
GSM7849489 Lung-Smad4ON_m1 Lung-Smad4ON_m
GSM7849490 Lung-Smad4ON_m2 Lung-Smad4ON_m
GSM7849491 Lung-Smad4ON_m3 Lung-Smad4ON_m

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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.