← BioTransfer GEO Dataset Finder
GEO series

Mutant RIT1 cooperates with YAP to drive an EMT-like lung cancer state [CUT&Run]

GSE302452 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 94 samples Submitted 2025/07/14 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 94 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)
Topic
Lung cancerThis series is part of our curated briefing on it.
Samples
94 samples: 94 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
Mutant RIT1 cooperates with YAP to drive an EMT-like lung cancer stateCell reports · 2025
Summary (written by the submitters)
The discovery of oncogene addiction in cancer has led to the development of over a dozen FDA-approved biomarker-driven therapies in lung adenocarcinoma, but many rare driver mutations remain untargeted. Somatic mutations of the “Ras-like in all tissues” (RIT1) gene are non-canonical driver events in lung cancer, occurring in ~2% of lung adenocarcinomas in a mutually exclusive fashion with KRAS and EGFR mutations.
Read the full summary ↓
Patients with RIT1-mutant lung cancer lack targeted therapy treatment options, and a lack of pre-clinical models has hindered the development of therapeutic strategies for RIT1-mutant lung cancer. Here we report a new mouse model with inducible regulation of the cancer-associated RIT1M90I variant. We show that autochthonous expression of RIT1M90I in the lung weakly promotes cancer alone or in combination with loss of the p53 tumor suppressor. However, potent synergy between RIT1M90I and inactivation of Nf2 and p53 drives an aggressive lung cancer with 100% penetrance and short latency. We show this oncogenic cooperation is driven by synergistic activation of cJUN, a component of the AP-1 complex. Therapeutic inhibition of MEK and YAP/TEAD suppressed RIT1M90I-driven lung cancer in vitro and in vivo. These data identify YAP/TEAD as an important mediator of RIT1’s oncogenic potential and nominate TEAD as an important drug target in RIT1-mutant lung cancer.
Published in
Mutant RIT1 cooperates with YAP to drive an EMT-like lung cancer state
Rominger MC, O'Brien S, Gupta S et al. · Cell reports 2025 · PMID 41004338 · doi:10.1016/j.celrep.2025.116185
This dataset
Open GSE302452 in finder → Find more human ChIP / ATAC / CUT&Tag datasets → Lung cancer landscape →
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 GSE302452_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 PRJNA1290948 and SRA study SRP600981. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41004338) ↗
Study design
94 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 94 sample titles: 94 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

+ 54 more — browse all 94 samples with per-sample file links →

More Lung cancer datasets

Search all human ChIP / ATAC / CUT&Tag datasets in GEO →

Share this dataset

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.