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EZH2 inhibition remodels the inflammatory senescence-associated secretory phenotype to potentiate pancreatic cancer immune surveillance

GSE201495 Mus musculus (mouse) RNA-seq (gene expression) 22 samples Submitted 2023/03/13 Platform GPL17021
At a glance

RNA-seq (gene expression) · mouse · 22 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
22 samples
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)
Immunotherapies that produce durable responses in some malignancies have failed in pancreatic ductal adenocarcinoma (PDAC) due to rampant immune suppression and poor tumor immunogenicity. We and others have demonstrated that induction of the senescence-associated secretory phenotype (SASP) can be an effective approach to activate anti-tumor Natural Killer (NK) and T cell immunity.
Read the full summary ↓
Here we found the pancreas tumor microenvironment (TME) suppresses NK and T cell surveillance following therapy-induced senescence through EZH2-mediated epigenetic repression of pro-inflammatory SASP genes. EZH2 blockade stimulated production of SASP chemokines CCL2 and CXCL9/10, leading to enhanced NK and T cell infiltration and PDAC eradication in mouse models. EZH2 activity was also associated with suppression of chemokine signaling and cytotoxic lymphocytes and reduced survival in PDAC patients. These results demonstrate that EZH2 represses of the pro-inflammatory SASP, and that EZH2 inhibition combined with senescence-inducing therapy could be a powerful means to achieve immune-mediated tumor control in PDAC.
This dataset
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.

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 GSE201495_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA831880 and SRA study SRP372183. Searching any of these in the dataset finder brings you back here.

Samples in this study

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+ 22 more — browse all 22 samples with per-sample file links →

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