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Genome-wide in vivo CRISPR screens identify GATOR1 as a potent tumor suppressor in MYC-driven lymphomagenesis

GSE285351 Mus musculus (mouse) RNA-seq (gene expression) 35 samples Submitted 2025/07/23 Platform GPL19057
At a glance

RNA-seq (gene expression) · mouse · 35 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
35 samples: 6 conditions, mostly with about 6 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.
Paper
Genome-wide in vivo CRISPR screens identify GATOR1 complex as a tumor suppressor in Myc-driven lymphomaNature communications · 2025
Summary (written by the submitters)
Identifying tumor suppressor genes is predicted to inform on the development of novel strategies for cancer therapy. To identify new lymphoma driving processes that cooperate with oncogenic MYC (which is abnormally highly expressed in ~70% of human cancers) we have used a genome-wide CRISPR knockout screen in Eµ-Myc;Cas9 transgenic hematopoietic stem and progenitor cells in vivo.
Read the full summary ↓
We discovered that loss of any of the GATOR1 complex components - NPRL3, DEPDC5, NPRL2 - significantly accelerated c-MYC-driven lymphoma development in mice. Low expression of the GATOR1 complex genes correlated with poor survival outcomes for human patients with high MYC-expressing cancers. Murine lymphomas lacking GATOR1 were highly sensitive to mTOR inhibitors as a single agent therapy, both in vitro and in vivo. These findings identify inhibition of mTORC1 as a potent tumor suppressive mechanism in c-MYC-driven lymphomagenesis and suggest a new avenue for therapeutic intervention in GATOR1-deficient lymphomas through mTOR inhibition.
Published in
Genome-wide in vivo CRISPR screens identify GATOR1 complex as a tumor suppressor in Myc-driven lymphoma
Potts MA, Mizutani S, Deng Y et al. · Nature communications 2025 · PMID 40841511 · doi:10.1038/s41467-025-62615-y
This dataset
Open GSE285351 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 GSE285351_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 PRJNA1203216 and SRA study SRP553956. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40841511) ↗
Study design
6 conditions, mostly with about 6 replicates each
Full Serum - Depdc5 KO ×6 Full Serum - Nprl3 KO ×6 Serum Deprivation - Depdc5 KO ×6 Serum Deprivation - Control ×6 Serum Deprivation - Nprl3 KO ×6 Full Serum - Control ×5

Supports a between-group comparison across 35 samples.

How we read this: 6 replicated groups read from 35 sample titles; they account for 35 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
Datasets on a similar topic

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