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T cell lymphoma associated STAT3 variants impose a type 1 regulatory phenotype

GSE328028 Mus musculus (mouse) RNA-seq (gene expression) 28 samples Submitted 2026/04/14 Platform GPL13112
At a glance

RNA-seq (gene expression) · mouse · 28 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
28 samples: 10 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.
Paper
T-cell lymphoma-associated STAT3 variants impose a type 1 regulatory-like phenotypeFrontiers in immunology · 2026
Summary (written by the submitters)
STAT3 signaling is fundamental to T cells, where it underlies basic cellular processes like metabolism and apoptosis, and specialized processes like differentiation and cytokine production. However, mutations of STAT3 are strikingly prevalent in T cell cancers and aberrant or excessive STAT3 signaling is thought to mobilize cellular pathways that encourage malignancy.
Read the full summary ↓
To better understand how STAT3 mutations drive T cell cancers, we compared two frequent cancer-associated variants, Y640F and N647I, at cellular and molecular levels. Using a retrogenic system, we demonstrate that they are qualitatively similar yet quantitatively distinct; each bears a gain-of-function phenotype but Y640F has greater transcriptome-wide effects. We also discovered that these and other common STAT3 mutants invoke a T regulatory 1 (Tr1) gene program characterized by expression of IL-10 and other factors that dampen T cell responses, most notably LAG3 and CD39. Importantly, Tr1 ’skewing’ is evident in both mouse T cells expressing cancer-associated STAT3 variants and humans afflicted with T cell malignancies. These studies advance current understanding of how cancer-associated mutations impact STAT3 function and reveal anti-inflammatory properties that may help transformed T cells persist, expand and/or avoid eradication.
Published in
T-cell lymphoma-associated STAT3 variants impose a type 1 regulatory-like phenotype
Schultz AB, Dalzell M, Nivelo L et al. · Frontiers in immunology 2026 · PMID 42164506 · doi:10.3389/fimmu.2026.1726565
This dataset
Open GSE328028 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 GSE328028_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 PRJNA1453535 and SRA study SRP691844. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42164506) ↗
Study design
10 conditions, mostly in duplicate
S3KO_CD8_MigR1 ×3 S3KO_CD8_STAT3.203 ×3 S3KO_CD4_MigR1 ×2 S3KO_CD4_STAT3.203 ×2 S3KO_CD4_Y640F ×2 S3KO_CD4_N647I ×2 S3KO_CD4_N647D ×2 S3KO_CD8_Y640F ×2 +8 more

Supports a between-group comparison across 22 samples.

How we read this: 10 replicated groups read from 28 sample titles; they account for 22 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
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