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Mis-splicing derived neoantigens and cognate T cell receptors in splicing factor mutant leukemias

GSE268157 Homo sapiens (human) RNA-seq (gene expression) 54 samples Submitted 2024/05/22 Platform GPL34284
At a glance

RNA-seq (gene expression) · human · 54 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
54 samples: 54 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
Mis-splicing-derived neoantigens and cognate TCRs in splicing factor mutant leukemiasCell · 2025
Summary (written by the submitters)
Mutations in RNA splicing factors are prevalent across cancers and generate recurrently mis-spliced mRNA isoforms. Here we identified a series of bona fide neoantigens translated from highly stereotyped splicing alterations promoted by neomorphic, leukemia-associated somatic mutations in the splicing machinery. We utilized feature-barcoded peptide-MHC dextramers to isolate neoantigen-specific T cell receptors (TCR) from both healthy donors and patients with leukemia.
Read the full summary ↓
While circulating neoantigen-specific CD8+ T cells were identified in patients with active disease, they were dysfunctional with reduced inflammatory response gene signatures. In contrast, donor CD8+ T cells with tumor-reactive TCRs were present following curative allogeneic hematopoietic cell transplant. T cells engineered with TCRs recognizing an SRSF2 mutant-induced neoantigen in CLK3 resulted in specific recognition and cytotoxicity of SRSF2 mutant leukemia. These data identify RNA mis-splicing derived neoantigens and neoantigen-specific TCRs across patients and provide proof-of-concept to genetically redirect T cells to public mis-splicing derived neoantigens in myeloid leukemias.
Published in
Mis-splicing-derived neoantigens and cognate TCRs in splicing factor mutant leukemias
Kim WJ, Crosse EI, De Neef E et al. · Cell 2025 · PMID 40273911 · doi:10.1016/j.cell.2025.03.047
This dataset
Open GSE268157 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 GSE268157_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 PRJNA1114778 and SRA study SRP509296. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40273911) ↗
Study design
54 samples, each with a distinct title — no replicated groups

Supports a case/control comparison: 45 samples read as cases, 9 as controls.

How we read this: Read from 54 sample titles: 54 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

+ 14 more — browse all 54 samples with per-sample file links →

Datasets on a similar topic

Search all human RNA-seq datasets in GEO →

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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.