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Tumor NLRP3 Amplification Promotes Immunotherapy Resistance by Inhibiting STAT1 Signaling and Suppressing MHC Class I Expression

GSE331449 Mus musculus (mouse) RNA-seq (gene expression) 17 samples Submitted 2026/07/21 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 17 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
17 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.
Summary (written by the submitters)
Immunotherapy resistance remains a significant challenge in immuno-oncology and predictive biomarkers are needed to guide combination immunotherapy selection for the individual patient. Using transcriptional signature analysis as well as accessible tissue and plasma-based assays, we show that elevated tumor-intrinsic NOD-, LRR-, and pyrin domain-containing protein 3 (NLRP3) signaling activity correlates with checkpoint inhibitor resistance in several independent cohorts of stage III/IV melanoma and gastroesophageal (GE) adenocarcinoma patients.
Read the full summary ↓
In situ hybridization studies demonstrate that tumor NLRP3 copy-number gain is observed in immunotherapy resistant melanomas and GE adenocarcinomas harboring enhanced NLRP3 signaling activity. Spatial transcriptomic analysis of GE adenocarcinomas reveals that NLRP3 signaling activity inversely correlates with NOD-, LRR-, and CARD-containing 5 (NLRC5) and major histocompatibility (MHC) class I-associated gene expression. Indeed, pre-clinical models of melanoma and GE adenocarcinoma demonstrate that Nlrp3 amplification suppresses NLRC5-mediated MHC class I upregulation, while pharmacologic NLRP3 inhibition augments tumor MHC class I surface levels. Upon activation, mechanistic studies reveal that NLRP3 binds to and inhibits STAT1 dimerization, nuclear translocation, and NLRC5 transcription. Consistent with these findings, inhibiting the NLRP3 inflammasome augments tumor STAT1-NLRC5 signaling, MHC class I surface expression, and overcomes anti-PD-1 resistance in an orthotopic model of microsatellite stable chromosomal instability (CIN) gastric adenocarcinoma. This work reveals a fundamental link between cellular stress and tumor-mediated immune evasion. In addition, these findings indicate that the tumor NLRP3 inflammasome signaling pathway merits further clinical study as a therapeutic target and a source of companion biomarkers for overcoming checkpoint inhibitor resistance in cancer patients.
This dataset
Open GSE331449 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 GSE331449_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 PRJNA1468151 and SRA study SRP702093. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
6 conditions, mostly in duplicate
set-2-control whole tumo ×3 set2-NLRP3A whole tumo ×3 Control CRISPR whole tumo ×2 NLRP3A CRISPR whole tumo ×2 WT whole tumo ×2 KO whole tumo ×2 Control CRISPR sorted cells ×1 NLRP3A CRISPR sorted cells ×1 +1 more

Supports a between-group comparison across 14 samples.

How we read this: 6 replicated groups read from 17 sample titles; they account for 14 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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