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Sleeping Beauty mutagenesis identifies BACH2 and other regulators promoting CD8+ T cell persistence and effector function under tumor-associated chronic antigen stimulation - esTAG

GSE302683 Mus musculus (mouse) RNA-seq (gene expression) 29 samples Submitted 2026/07/22 Platform GPL34475
At a glance

RNA-seq (gene expression) · mouse · 29 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
29 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
Sleeping Beauty mutagenesis identifies BACH2 and other regulators of CD8(+) T-cell exhaustion, persistence in vivo, and CAR-T cell function under tumor-associated chronic antigen stimulationJournal for immunotherapy of cancer · 2026
Summary (written by the submitters)
● Background: Genes that enhance T cell function could represent promising targets for improving engineered T cell therapies for cancer. While extensive CRISPR knockout screens have identified key genes that enhance T cell persistence, employing Sleeping Beauty (SB) insertional mutagenesis, which induces both gain- (GOF) and loss-of-function (LOF) mutations via the generation of fusion transcripts with endogenous genes, may uncover additional critical factors that previous approaches have overlooked.
Read the full summary ↓
● Methods: We developed transgenic mouse models carrying Doxycycline (Dox)-inducible SB engineered system (DiSBey) in primary T cells. Using DiSBey, we conducted screens to identify genes that enhance T cell persistence under chronic antigen exposure. Specifically, CD8⁺ T cells from Dox-fed DiSBey mice were subjected to repeated anti-CD3 stimulation over 18 days to mimic chronic antigenic stimulation. We then identified SB transposon genomic insertion sites and corresponding fusion transcripts from the persistent DiSBey CD8⁺ T cells using enhanced-specificity tagmentation sequencing (esTag-seq) and RNA-seq, respectively. ● Results: Under chronic stimulation, SB-mutagenized CD8⁺ T cells exhibited improved persistence and reduced terminal exhaustion phenotype. Across six independent screens, we identified 38 genes that were recurrently targeted by the SB transposon T2/Onc2 and differentially expressed under chronic anti-CD3 stimulation stress. Among these, T2/Onc2 insertions into Bach2 and Elmo1 were repeatedly found at the genomic level and were associated with altered nascent transcript expression. Bach2, previously recognized as a key regulator of T cell memory formation and resistance to chronic viral infection but less characterized in engineered T cells for cancer therapy, was found to enhance in vivo persistence in the B16-Ova tumor model. We further demonstrated that ectopic Bach2 expression levels influence engineered T cell differentiation lineage. A Bach2low signature allowed differentiation into both KLRG1⁺ and CD62L⁺ phenotypes, whereas Bach2high restricted differentiation predominantly to the CD62L⁺ subset. Finally, in human CART19-28ζ cells, BACH2 overexpression enhanced cytotoxicity and improved tumor control following chronic cancer stimulation in vivo. ● Conclusions: Controllable SB mutagenesis using DiSBey mice provides a novel platform for functional screening of genes that improve T cell therapeutic phenotypes. Our findings highlight a dose-dependent role of BACH2 in enhancing the function of engineered T cells under conditions of chronic antigenic stimulation.
Published in
Sleeping Beauty mutagenesis identifies BACH2 and other regulators of CD8(+) T-cell exhaustion, persistence in vivo, and CAR-T cell function under tumor-associated chronic antigen stimulation
Lee CJ, Larsson AT, Jubenville TA et al. · Journal for immunotherapy of cancer 2026 · PMID 42457339 · doi:10.1136/jitc-2025-013536
This dataset
Open GSE302683 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 GSE302683_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 PRJNA1291717 and SRA study SRP601002. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42457339) ↗
Study design
10 conditions, mostly in duplicate
CD8+ T Cells, DiSBey, Chronic Stimulation, Da… ×4 CD8+ T Cells, DiSBey, Acute Stimulation, Day … ×4 CD8+ T Cells, DiSBey, Acute Stimulation, Day … ×3 CD8+ T Cells, DiSBey, Acute Stimulation, Day … ×3 CD8+ T Cells, DiSBey, Acute Stimulation, Day … ×3 CD8+ T Cells, DiSBey, Chronic Stimulation, Da… ×2 CD8+ T Cells, DiSBey, Acute Stimulation, Day … ×2 CD8+ T Cells, DiSBey, Chronic Stimulation, Da… ×2 +4 more

Supports a between-group comparison across 27 samples.

How we read this: 10 replicated groups read from 29 sample titles; they account for 27 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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