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EZH1/2 inhibition improves immunotherapy response through MHC Class II de-repression and neutrophil reprogramming

GSE330733 Mus musculus (mouse) RNA-seq (gene expression) 11 samples Submitted 2026/07/01 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 11 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
11 samples: 11 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.
Summary (written by the submitters)
Squamous cell carcinoma of the lung is a difficult-to-treat cancer with high prevalence in the US, and particularly in Kentucky. The goals of this work were to test if the EZH1/2 inhibitor valemetostat improves anti-PD1 responses in squamous cell lung cancer models, and to develop ex vivo models to test immunotherapy drug combinations.
Read the full summary ↓
We found that valemetostat produced augmented anti-tumor responses to anti-PD1 therapy through up-regulation of tumor cell specific Major Histocompatibility Complex Class II (MHC Class II), and a shift towards activated CD8+ T cells . Neutrophils predominated in these tumors regardless of therapy, but examination of bone marrow revealed that valemetostat treated mice and mice that rejected tumors both had more mature neutrophils. Likewise, Ezh2 knock-out mice produced neutrophils that were more apoptotic, less migratory, and less able to produce extracellular nets, but had similar ability to kill bacteria as Ezh2-WT neutrophils. To test tumor responses to differing neutrophil populations, we engineered three-dimensional air-liquid interface cultures with tumoroids, lung mesenchymal cells, and T cells, with and without bone marrow containing neutrophils and myeloid progenitors from distinct donors. Bone marrow from tumor-naïve and mice with actively growing untreated tumors boosted tumoroid growth, while bone marrow from tumor-rejected and mice with tumors treated with valemetostat was anti-tumor. MHC Class II blockade lowered the ability of bone marrow to boost tumor growth and reduced the ability of valemetostat with anti-PD1 to reduce tumoroid growth. Patient samples revealed a strong negative correlation between EZH2 and MHC Class II, suggesting that targeting EZH2 activity could lead to marked increase in MHC Class II and improve treatment responses in lung squamous cell carcinomas.
This dataset
Open GSE330733 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 GSE330733_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 PRJNA1465347 and SRA study SRP699732. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
11 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 11 sample titles: 11 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
Datasets on a similar topic

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