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Loss of NOTCH2 Creates a TRIM28-Dependent Vulnerability in Small Cell Lung Cancer [bulk_murine_RNA_seq]

GSE297361 Mus musculus (mouse) RNA-seq (gene expression) 36 samples Submitted 2025/07/10 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 36 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
36 samples: 16 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)
Small cell lung cancer (SCLC) is a highly aggressive malignancy that lacks effective targeted therapies, in part due to frequent loss-of-function mutations in tumor suppressors and the absence of recurrent oncogenic drivers. Approximately 15% of SCLCs harbor inactivating mutations in NOTCH1 or NOTCH2, and most neuroendocrine-high SCLCs exhibit low NOTCH activity.
Read the full summary ↓
Using CRISPR/Cas9 screening in primary cell lines derived from NOTCH1/2-isogenic SCLC genetically engineered mouse models, we identified TRIM28 as a synthetic lethal dependency in NOTCH2-inactivated SCLCs. Loss of TRIM28 in this context robustly induced expression of endogenous retroviruses, activated viral sensing pathways, and triggered a type I interferon response. Mechanistically, NOTCH2 inactivation increased reliance on TRIM28-mediated ERV silencing, creating a hyper-dependence on TRIM28 via the STING–MAVS–TBK1 axis. Notably, TRIM28 was essential for tumor growth only in the setting of NOTCH2 loss. These findings identify TRIM28 as a potential therapeutic target in NOTCH2-deficient or low-NOTCH2-expressing SCLC.
This dataset
Open GSE297361 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 GSE297361_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 PRJNA1263603 and SRA study SRP585718. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
16 conditions, mostly in duplicate
Adherent_K93_NT ×2 Adherent_K93_Trim28sg1 ×2 Adherent_K93_Trim28sg2 ×2 Adherent_K60_NT ×2 Adherent_K60_Trim28sg2 ×2 Suspension_K93_NT ×2 Suspension_K93_Trim28sg1 ×2 Suspension_K93_Trim28sg2 ×2 +12 more

Supports a between-group comparison across 32 samples.

How we read this: 16 replicated groups read from 36 sample titles; they account for 32 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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