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Interleukin-4 modulates type I interferon to augment anti-tumour immunity

GSE289722 Mus musculus (mouse) RNA-seq (gene expression) 30 samples Submitted 2025/02/18 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 30 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
30 samples: 10 × B16-GFP Tumours (Day 8) biol vs 10 × B16-IFNA4 Tumours (Day 8) biol vs 10 × B16-IFNA9 Tumours (Day 8) biolGroups 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
Interleukin-4 modulates type I interferon to augment antitumor immunityScience advances · 2025
Summary (written by the submitters)
Despite advances in immunotherapy, metastatic melanoma remains a significant therapeutic challenge due to the complexity of the tumour microenvironment. Intratumoural type I interferon (IFN-I) has long been associated with improved clinical outcomes. However, several IFN-I subtypes can also paradoxically promote tumour growth in some contexts.
Read the full summary ↓
We investigated this further by engineering murine B16 melanoma cells to overexpress various IFN-I subtypes, where a spectrum of outcomes was observed. Characterisation of these tumours by RNA-sequencing revealed a tumour immune phenotype, where potent IFN-I signalling concomitant with diminished type 2 inflammation failed to confer durable tumour control. T cell-mediated rejection of these tumours was restored by introducing interleukin (IL)-4 into the tumour microenvironment, either through ectopic expression or in a pre-clinical adoptive T cell therapy model. Collectively, our findings highlight the IFN-I/IL-4 axis in promoting anti-tumour immunity, which could be harnessed to target and stratify solid tumours that are non-responsive to frontline therapies.
Published in
Interleukin-4 modulates type I interferon to augment antitumor immunity
Newnes HV, Armitage JD, Buzzai AC et al. · Science advances 2025 · PMID 40367186 · doi:10.1126/sciadv.adt3618
This dataset
Open GSE289722 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 GSE289722_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 PRJNA1224251 and SRA study SRP564164. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40367186) ↗
Study design
10 × B16-GFP Tumours (Day 8) biol vs 10 × B16-IFNA4 Tumours (Day 8) biol vs 10 × B16-IFNA9 Tumours (Day 8) biol

Supports a between-group comparison across 30 samples.

How we read this: 3 replicated groups read from 30 sample titles; they account for 30 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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