GEO series

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

GSE244944 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 18 samples Submitted 2025/06/02 Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 18 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)
Samples: 18 samples: 18 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 GSE244944 in finder → Find more human ChIP / ATAC / CUT&Tag 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 GSE244944_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 PRJNA1026473 and SRA study SRP465377. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40627448) ↗

Study design

18 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 18 sample titles: 18 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

GSM7832398 1_CORL88_DMSO_H3K27Ac_1
GSM7832399 2_CORL88_DMSO_H3K27Ac_2
GSM7832400 3_CORL88_DMSO_input
GSM7832401 4_CORL88_TAS1440_GSI_H3K27Ac_1
GSM7832402 5_CORL88_TAS1440_GSI_H3K27Ac_2
GSM7832403 6_CORL88_TAS1440_GSI_input
GSM7832404 7_CORL88_TAS1440_H3K27Ac_1
GSM7832405 8_CORL88_TAS1440_H3K27Ac_2
GSM7832406 9_CORL88_TAS1440_input
GSM7832407 10_H82_NOTCH1_dox_H3K27Ac_1
GSM7832408 11_H82_NOTCH1_dox_H3K27Ac_2
GSM7832409 12_H82_NOTCH1_dox_input
GSM7832410 13_H82_NOTCH1_no_dox_H3K27Ac_1
GSM7832411 14_H82_NOTCH1_no_dox_H3K27Ac_2
GSM7832412 15_H82_NOTCH1_no_dox_input
GSM7832413 16_H82_parental_H3K27Ac_1
GSM7832414 17_H82_parental_H3K27Ac_2
GSM7832415 18_H82_parental_input

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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.