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Gene expression profiles of CAR-T cells expressing constitutively active mutants of STAT3 and STAT5 [STAT3_5_CART_in_vivo]

GSE296802 Homo sapiens (human) RNA-seq (gene expression) 12 samples Submitted 2026/02/19 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 12 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
Homo sapiens (human)
Samples
12 samples: 12 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
Constitutive STAT3 Signaling, in Comparison with STAT5, Enhances CAR T-cell Efficacy and Lowers Systemic ToxicityCancer immunology research · 2026
Summary (written by the submitters)
Augmenting cytokine signaling represents a pivotal strategy to enhance the therapeutic efficacy of chimeric antigen receptor (CAR) T cell therapy. However, the distinct roles of the downstream transcription factors STAT3 and STAT5 remain insufficiently defined. To elucidate their individual contributions, we engineered CAR-T cells to express constitutively active mutants of STAT3 (Y640F; caSTAT3) or STAT5 (N642H; caSTAT5).
Read the full summary ↓
In vitro, caSTAT3 CAR-T cells demonstrated superior effector functions and a pronounced memory-associated phenotype, accompanied by broader transcriptomic alterations compared to their caSTAT5 counterparts. Despite impaired proliferative capacity in vitro, caSTAT3 CAR-T cells exhibited reduced systemic toxicity and sustained antitumor activity across multiple in vivo tumor models. In contrast, caSTAT5 CAR-T cells showed efficient intratumoral accumulation but also disseminated into non-malignant tissues, resulting in fatal toxicity. Notably, co-expression of caSTAT3 and caSTAT5 synergistically promoted both effector function and long-term persistence of CAR-T cells. Collectively, these findings provide mechanistic insights into the differential roles of STAT3 and STAT5 signaling in shaping the functional and safety profiles of CAR-T cell therapies.
Published in
Constitutive STAT3 Signaling, in Comparison with STAT5, Enhances CAR T-cell Efficacy and Lowers Systemic Toxicity
Zhang H, Ito Y, Kasuya H et al. · Cancer immunology research 2026 · PMID 41758968 · doi:10.1158/2326-6066.CIR-25-0611
This dataset
Open GSE296802 in finder → Find more human 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 GSE296802_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 PRJNA1261066 and SRA study SRP584228. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41758968) ↗
Study design
12 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 12 sample titles: 12 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.