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NOTCH1 reverses immune suppression in small cell lung cancer through reactivation of STING [RNA-seq]

GSE244945 Homo sapiens (human) RNA-seq (gene expression) 69 samples Submitted 2025/06/02 Platform GPL24247Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 69 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
Homo sapiens (human)
Samples
69 samples: 69 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
NOTCH1 reverses immune suppression in small cell lung cancer through reactivation of STINGThe Journal of clinical investigation · 2025
Summary (written by the submitters)
Downregulation of antigen presentation and lack of immune infiltration are defining features of small cell lung cancer (SCLC) limiting response to immune checkpoint blockade (ICB), While a high MHC Class I, immune-inflamed subset benefits from ICB, underlying mechanisms of immune response in SCLC have yet to be elucidated.
Read the full summary ↓
Here we show that in the landmark IMpower133 clinical trial high, but not low, NOTCH1 expression is significantly associated with longer survival with the addition of ICB to chemotherapy among ~80% of SCLC patients with neuroendocrine-enriched tumors (ASCL1-enriched, HR 0.39, P=0.0012; NEUROD1-enriched, HR 0.44, P=0.024). Genetic or pharmacologic activation of NOTCH1 in ASCL1 and NEUROD1 SCLC cell lines dramatically up-regulates MHC Class I through epigenetic up-regulation of STING. In syngeneic mouse models, Notch1 activation reprograms SCLC tumors from immune-excluded to immune-inflamed, facilitating durable, complete responses with ICB combined with a STING agonist. STING1 expression is significantly enriched in high compared to low NOTCH1 expressing tumors in IMpower133 thereby validating our proposed mechanism. Our data reveal a previously undiscovered role for NOTCH1 as a critical driver of SCLC immunogenicity and a potential predictive biomarker for ICB in SCLC. NOTCH1 activation may be a therapeutic strategy to unleash anti-tumor immune responses in SCLC and other neuroendocrine cancers in which NOTCH1 is typically suppressed.
Published in
NOTCH1 reverses immune suppression in small cell lung cancer through reactivation of STING
Kim YS, Nabet BY, Cortez BN et al. · The Journal of clinical investigation 2025 · PMID 40627448 · doi:10.1172/JCI185423
This dataset
Open GSE244945 in finder → Find more 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 GSE244945_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 PRJNA1026472 and SRA study SRP465376. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40627448) ↗
Study design
69 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 69 sample titles: 69 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

+ 29 more — browse all 69 samples with per-sample file links →

Datasets on a similar topic

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