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Extracellular vesicles from obese visceral adipose promote pancreatic cancer development and resistance to immune checkpoint blockade therapy [ATAC-seq]

GSE254919 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/10/28 Platform GPL24247
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 12 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
12 samples: 6 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)
Obesity is correlated with multitype of cancer development, and pancreatic ductal adenocarcinoma (PDAC) in obese patients often shows dismal prognosis and resistance to immune checkpoint blockade (ICB) therapy. The molecular mechanism for these phenomena is largely unknown. Here we show that obese visceral adipose tissues (VAT) can talk to distant PDAC by delivering their extracellular vesicle (EV) carrying signal molecules.
Read the full summary ↓
We reveal that PDAC cells can take in VAT-EVs to their lysosomes where EVs-delivered Cathepsin A (Ctsa) stabilize ribonuclease Rnaset2b to produce extra pseudouridines from RNA cleavage. Pseudouridines released from cancer cells activate mast cells which inhibit CD8+ T cell activity, forming an immunosuppressive tumor microenvironment which enhances cancer progression and resistance to ICB. We also demonstrate the harmful effects of VAT-EV CTSA-pseudouridine-mast cell axis for PDAC in obese patients. Animal experiments indicate that Ctsa knockdown effectively enhances the ICB efficacy on PDAC. Our study uncovers a mechanism connecting obesity and cancer which holds promise for developing new therapeutic strategy for obesity related cancers.
This dataset
Open GSE254919 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 GSE254919_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 PRJNA1072629 and SRA study SRP487767. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
MC9-cell_Vehicle_ATAC-Seq ×2 MC9-cell_PseudoU_ATAC-Seq ×2 KPC-cell_Vehicle_ATAC-Seq ×2 KPC-cell_PseudoU_ATAC-Seq ×2 CD8T-cell_Vehicle_ATAC-Seq ×2 CD8T-cell_PseudoU_ATAC-Seq ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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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