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HNF4a controls growth, identity and response to KRAS inhibition in IMA [bulk RNAseq]

GSE294570 Mus musculus (mouse) RNA-seq (gene expression) 16 samples Submitted 2026/04/23 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 16 samples

What it is
RNA-seq (gene expression)Gene activity measured by sequencing. The summary does not mention single-cell work, so this is most likely bulk: one average per sample, the usual starting point for comparing groups.
Organism
Mus musculus (mouse)
Samples
16 samples: 16 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.
Summary (written by the submitters)
Cellular plasticity is a key feature of cancer that enables tumor cells to switch lineage identities, driving disease progression and resistance to therapy. In invasive mucinous adenocarcinoma (IMA), a pronounced pulmonary-to-gastric lineage switch occurs as NKX2.1 is lost and HNF4α, a master regulator of gastric differentiation, is upregulated.
Read the full summary ↓
Although absent in normal alveolar type 2 cells, HNF4α is aberrantly expressed in most IMA tumors. In this study, we investigate the role of HNF4α within established IMAs and find that it directly activates a gastric differentiation program, particularly in pit cells. Loss of HNF4α disrupts this program and permits FOXA1 and FOXA2 to bind new regulatory sites, leading to the upregulation of neuronal and liver-like gene modules. In light of the poor response of mucinous tumors to KRAS inhibitors, we investigated whether HNF4α modulates sensitivity to KRASG12D targeted therapy. Treatment with MRTX1133 revealed that loss of HNF4α significantly increases IMA sensitivity by reducing the IC50 in vitro and enhancing tumor regression in vivo. Mechanistically, HNF4α deletion impairs cell-cycle progression in drug-tolerant persister cells, while tumors retaining HNF4α maintain these cell-cycle regulators despite KRAS inhibition, promoting persister cell survival. Our findings establish HNF4α as a critical driver of IMA biology governing both gastric differentiation and resistance to KRAS inhibitors and support the development of combination strategies to overcome therapeutic resistance in IMA.
This dataset
Open GSE294570 in finder → Find more mouse RNA-seq 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 GSE294570_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: For RNA-seq the counts are usually a table among the supplementary files (look for 'counts', 'expression', 'FPKM' or 'TPM' in the file name). The series matrix of a sequencing study is sometimes nearly empty.

Also filed as BioProject PRJNA1250517 and SRA study SRP578522. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 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 16 sample titles: 16 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
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.