GEO series

Prolonged KRAS-MAPK Inhibition Induces Interferon-mediated Epithelial-to-Mesenchymal Transition and Reveals Therapeutic Opportunities [cut and run-ChipSeq]

GSE303106 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2026/03/25 Platform GPL34284

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 9 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)
Samples: 9 samples: 9 samples, each with a distinct title — no replicated groupsGroups 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: Prolonged KRAS-MAPK Inhibition Induces Interferon Signaling That Promotes Cell State Transition and Confers Therapeutic VulnerabilitiesCancer research · 2026

Summary (written by the submitters)

Direct targeting of KRAS to inhibit the MAPK cascade is poised to transform the therapeutic landscape of pancreatic ductal adenocarcinoma (PDAC). However, analyses of PDAC samples from patients treated with KRASG12C inhibitors have revealed secondary mutations restoring MAPK signaling and promoting epithelial-to-mesenchymal transition (EMT) as key mechanisms of acquired resistance.

Read the full summary ↓

Here, we show that human PDAC specimens and patient-derived cell lines treated with the ERK inhibitor ulixertinib display upregulation of EMT-associated and inflammatory gene programs, particularly those linked to the interferon response. Using the GeneRep-nSCORE computational framework to reconstruct and rank regulatory networks enriched in ERKi and KRASi-resistant PDAC cells, we identified TRIM22, an interferon-inducible E3 ubiquitin ligase, as a critical mediator of EMT and treatment resistance. Mechanistically, TRIM22 promotes proteasomal degradation of IκBα, leading to activation of NF-κB signaling, a known driver of EMT. Notably, we identify TACSTD2, encoding Trophoblast Cell Surface Antigen 2 (TROP2), as an NF-κB target gene upregulated following EMT. Functionally, combining ulixertinib or the KRAS inhibitor MRTX1133 with the TROP2-directed antibody-drug conjugate sacituzumab govitecan significantly suppressed the growth of PDAC patient-derived xenografts. Together, our work identifies TRIM22 as a molecular link between interferon signaling and EMT and reveals TROP2 as a druggable vulnerability to sustain the therapeutic efficacy of KRAS-MAPK pathway inhibitors.

Published in

Prolonged KRAS-MAPK Inhibition Induces Interferon Signaling That Promotes Cell State Transition and Confers Therapeutic Vulnerabilities

Bulle A, Chen Y, Li H et al. · Cancer research 2026 · PMID 42008116 · doi:10.1158/0008-5472.CAN-25-4114

This dataset

Open GSE303106 in finder → Find more human 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 GSE303106_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 PRJNA1294187 and SRA study SRP602345. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42008116) ↗

Study design

9 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 9 sample titles: 9 distinct titles with little repetition. 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

GSM9117972 Input-Untretaed
GSM9117973 IRF1-Untretaed
GSM9117974 IRF1-Ulixertinib
GSM9117975 IRF1-MRTX1133
GSM9117976 IRF9-Untretaed
GSM9117977 IRF9-Ulixertinib
GSM9117978 IRF9-MRTX1133
GSM9117979 NF-kBp65-Untretaed
GSM9117980 NF-kBp65-Ulixerinib

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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.