GEO series

SMARCA4-mutant lung cancer disrupts anti-tumor immunity and immunotherapy response in a STING pathway-dependent manner [CUT&RUN]

GSE278869 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 12 samples Submitted 2025/02/26 Platform GPL18573

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 12 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: 12 samples: 6 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.
Paper: Mutation of SMARCA4 Induces Cancer Cell-Intrinsic Defects in the Enhancer Landscape and Resistance to ImmunotherapyCancer research · 2025

Summary (written by the submitters)

Genomic studies have identified frequent alterations in components of the SWI/SNF (SWItch/Sucrose Non- Fermenting) chromatin remodeling complex including SMARCA4 and ARID1A. Recent independent clinical studies have shown that SMARCA4 mutant lung cancer patients have very poor response to immunotherapy but the mechanism is unknown. Here we showed that SMARCA4 deficiency caused resistance to anti-PD1 immunotherapy and decreased CD4+ T cell and conventional class I dendritic cells (“cDC1s”) percentage in tumor microenvironment (TME) with mouse models.

Read the full summary ↓

SMARCA4 loss in tumor cells prevented STING pathway sensing cytosolic DNA and cGAMP to reduce type I IFN and inflammatory cytokine gene expression. Importantly, SMARCA4 degradation induced a profound reprograming of the enhancer landscape with marked loss of chromatin accessibility at enhancers of genes involved in type I IFN and inflammatory cytokine, which is associated with nuclear factor NF-κB pathway. Our finding reveals that SMARCA4 loss can have a critical immune modulatory impact in cancer cell intrinsic fashion and suggest that manipulation of SMARCA4 in tumor cells can improve cancer immunotherapy.

Published in

Mutation of SMARCA4 Induces Cancer Cell-Intrinsic Defects in the Enhancer Landscape and Resistance to Immunotherapy

Wang Y, Meraz IM, Qudratullah M et al. · Cancer research 2025 · PMID 40080526 · doi:10.1158/0008-5472.CAN-24-2054

This dataset

Open GSE278869 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 GSE278869_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 PRJNA1169332 and SRA study SRP536778. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40080526) ↗

Study design

6 conditions, mostly in duplicate

CUT-RUN-NEG-D ×2 CUT-RUN-NFKB-D ×2 CUT-RUN-NFKB-A ×2 CUT-RUN-SM4-D ×2 CUT-RUN-SM4-A ×2 CUT-RUN-POS-D ×2

Supports a between-group comparison across 12 samples.

How we read this: 6 replicated groups read from 12 sample titles; they account for 12 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

GSM8556321 CUT-RUN-NEG-D1 CUT-RUN-NEG-D
GSM8556322 CUT-RUN-NEG-D2 CUT-RUN-NEG-D
GSM8556323 CUT-RUN-NFKB-D1 CUT-RUN-NFKB-D
GSM8556324 CUT-RUN-NFKB-D2 CUT-RUN-NFKB-D
GSM8556325 CUT-RUN-NFKB-A1 CUT-RUN-NFKB-A
GSM8556326 CUT-RUN-NFKB-A2 CUT-RUN-NFKB-A
GSM8556327 CUT-RUN-SM4-D1 CUT-RUN-SM4-D
GSM8556328 CUT-RUN-SM4-D2 CUT-RUN-SM4-D
GSM8556329 CUT-RUN-SM4-A1 CUT-RUN-SM4-A
GSM8556330 CUT-RUN-SM4-A2 CUT-RUN-SM4-A
GSM8556331 CUT-RUN-POS-D1 CUT-RUN-POS-D
GSM8556332 CUT-RUN-POS-D2 CUT-RUN-POS-D

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

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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.