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A novel small molecule Enpp1 inhibitor improves tumor control following radiation therapy by targeting stromal Enpp1 expression

GSE264328 Mus musculus (mouse) RNA-seq (gene expression) 37 samples Submitted 2024/10/25 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 37 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
37 samples: 37 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
A novel small molecule Enpp1 inhibitor improves tumor control following radiation therapy by targeting stromal Enpp1 expressionScientific reports · 2024
Summary (written by the submitters)
Through variation in the cancer cell and their immune infiltrates each tumor represents a unique problem, but therapeutic targets can be found in the shared features. Radiation therapy can change the interaction between the cancer cells and the stroma through release of innate adjuvants including the STING agonist cGAMP. Enpp1 is a phosphodiesterase that can be expressed by cancer cells and can degrade cGAMP.
Read the full summary ↓
Enpp1 can therefore limit innate adjuvant availability following radiation therapy. We observed that many cancer cells lack Enpp1 expression, but that Enpp1 expression is retained in cells of the tumor stroma and this expression in the tumor stroma limits tumor control by radiation therapy. We demonstrate the efficacy of a novel Enpp1 inhibitor and show that this inhibitor improves tumor control by radiation even where the cancer cells lack Enpp1. We show that the mechanism requires STING and type I IFN receptor expression by non-cancer cells, and is dependent on CD8 T cells as a final effector mechanism of tumor control. This suggests that Enpp1 inhibition may be an effective partner for radiation therapy regardless of whether cancer cells express Enpp1, and identifies a novel therapeutic Enpp1 inhibitor suitable for combination therapies in immuno-oncology.
Published in
A novel small molecule Enpp1 inhibitor improves tumor control following radiation therapy by targeting stromal Enpp1 expression
Baird JR, Alice AF, Saito R et al. · Scientific reports 2024 · PMID 39622844 · doi:10.1038/s41598-024-80677-8
This dataset
Open GSE264328 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 GSE264328_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 PRJNA1101982 and SRA study SRP502646. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39622844) ↗
Study design
37 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 37 sample titles: 37 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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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.