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Molecular dynamics driving phenotypic divergence among KRAS mutants in pancreatic tumorigenesis [ATAC-Seq]

GSE279088 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 74 samples Submitted 2026/04/01 Platform GPL21103Platform GPL30172Platform GPL17021
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 74 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
74 samples: 20 conditions, mostly with about 4 replicates eachGroups 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)
Inflammation in the context of pancreatic injury drives acinar-to-ductal metaplasia (ADM), a cell state that can be hijacked by mutant Kras to drive the formation of pancreatic cancer. ADM has been described alternatively to bear progenitor features, but the extent to which lineage reversion is a broad feature of the response to injury and early tumorigenesis – particularlyacross the different mutant Kras alleles – remains poorly understood.
Read the full summary ↓
Herein we implement novel Ptf1a-tdTomato mice to characterize early and late pancreatic progenitors to serve as benchmarks that define cell fate decisions in the context of inflammatory injury and initiation. In so doing, we define ADM as a mixed-lineage transcriptomic and epigenetic state, harboring ductal features but also featuring a lineage reversion to a late progenitor program. By contrast, while inducible mutant Kras alone is able to induce oncogenic gene expression, it does little to alter chromatin in the absence of injury. The most frequent Kras mutant alleles – G12D, G12V, and G12R – display differential cooptation of the injury-induced mixed-lineage state, with mutant KrasG12D able to form ADM and PanIN more robustly than either KrasG12V or KrasG12R. In particular, KrasG12R fails to promote phenotypic transformation on its own or even in concert with inflammatory injury. Whereas KrasG12Dand injury induce enhancer reprogramming at AP-1 chromatin and a lineage reversion to resemble early progenitors, KrasG12Rcan initiate but not sustain the key cell-intrinsic transcriptional and epigenetic programs characteristic of neoplasia. In KrasG12R we identify a decrease in Rac1 signaling, not AKT activation, as the key molecular defect. Together, these data demonstrate that the PDAC-associated Kras variants are not functionally equivalent and distinctly drive lineage reversion and epigenetic reprogramming, such that allele-specific approaches will be essential to therapeutic targeting in pancreatic cancer.
This dataset
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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 GSE279088_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 PRJNA1170339 and SRA study SRP537210. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
20 conditions, mostly with about 4 replicates each
wk3_KrasG12D_Caerulein_B ×6 wk3_KrasG12R_Caerulein_B ×5 wk3_KrasG12R_Saline_B ×5 d2_KrasG12D_Caerulein_B ×4 d2_KrasG12R_Caerulein_B ×4 d2_KrasG12Vgeo_Caerulein_B ×4 d2_KrasWT_Caerulein_B ×4 ductal ×4 +18 more

Supports a between-group comparison across 68 samples.

How we read this: 20 replicated groups read from 74 sample titles; they account for 68 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

+ 34 more — browse all 74 samples with per-sample file links →

Datasets on a similar topic

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