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Targeting KRAS in a TRP53/KRAS mouse and organoid model inhibits Barrett Esophagus to Gastroesophageal Adenocarcinoma progression

GSE333534 Mus musculus (mouse) RNA-seq (gene expression) 20 samples Submitted 2026/06/05 Platform GPL34328Platform GPL21626
At a glance

RNA-seq (gene expression) · mouse · 20 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
20 samples: 7 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)
The lack of experimental systems that faithfully model the genetic and phenotypic progression from Barrett’s esophagus (BE) to gastroesophageal adenocarcinoma (GEAC) has limited mechanistic discovery and preclinical testing. Here, we report a refined L2-IL-1B based mouse model engineered to capture two hallmark alterations of human GEAC: Trp53 loss and elevated KRAS signalling driven by KRAS amplification, modelled through oncogenic KrasG12D activation.
Read the full summary ↓
This combinatorial genetic approach accelerates malignant transformation and enables controlled, stage-resolved analysis of carcinogenesis at the gastroesophageal junction (GEJ). Parallel murine and human organoid systems recapitulate these in vivo phenotypes and provide a platform for functional interrogation of KRAS amplification, a frequent but understudied driver in GEAC. Using these organoids as treatment avatars in addition to the mouse model, we demonstrate that KRAS dependent growth is selectively vulnerable to combined SHP2 + MEK1/2 or ERK1/2 inhibition, revealing a therapeutically targetable signalling axis. Together, these integrated in vivo and organoid platforms represent a technical advance for modelling BE progression, dissecting KRAS pathway biology, and evaluating targeted strategies for KRAS amplified GEAC.
This dataset
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.

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 GSE333534_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1471267 and SRA study SRP704262. Searching any of these in the dataset finder brings you back here.

Study design
7 conditions, mostly in duplicate
L2-L_1 ×3 L2-L_2 ×3 L2-LKP_2 ×3 L2-LP_1 ×2 L2-LP_2 ×2 L2-LKP_1 ×2 L2-LKP_3 ×2 L2-L_3 ×1 +2 more

Supports a between-group comparison across 17 samples.

How we read this: 7 replicated groups read from 20 sample titles; they account for 17 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
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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.