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Tp53 and Tet2 mutations cooperatively transform progenitors (RNA-Seq)

GSE279386 Mus musculus (mouse) RNA-seq (gene expression) 24 samples Submitted 2025/05/12 Platform GPL21103
At a glance

RNA-seq (gene expression) · mouse · 24 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
24 samples: 24 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
TP53 mutations and TET2 deficiency cooperate to drive leukemogenesis and establish an immunosuppressive environmentThe Journal of clinical investigation · 2025
Summary (written by the submitters)
Mutations and deletions in TP53 are associated with adverse outcome in patients with myeloid malignancies and developing improved therapies for TP53-mutant leukemias is of urgent need. Here we identify mutations in TET2 as the most commonly co-existing mutation in TP53 mutant acute myeloid leukemia (AML) patients. Combined hematopoietic-specific deletion of TET2 and TP53 in mice enhanced self-renewal compared to deletion of either gene alone.
Read the full summary ↓
Tet2/Tp53 double knockout mice developed serially transplantable AML. Both mice as well as patients with AML and combined TET2/TP53 alterations upregulated innate immune signaling in malignant cells. Mice with TET2/TP53 loss had expansion of monocytic myeloid-derived suppressor cells which impaired T cell proliferation. Moreover, patients and mice with TP53/TET2 double mutant AML upregulated TIGIT ligands CD155 and CD112 on malignant cells. TIGIT blocking antibodies augmented the ability of NK cells to kill Tet2/Tp53 double mutant AML cells, reduced leukemic burden, and extended the survival of TET2/TP53 double knockout mice. These data thereby identify a previously unexplored link between TET2 and TP53 mutations and highlight therapeutic means to overcome the immunosuppressive bone marrow environment in this adverse subtype of AML.
Published in
TP53 mutations and TET2 deficiency cooperate to drive leukemogenesis and establish an immunosuppressive environment
Zhang P, Whipp EC, Skuli SJ et al. · The Journal of clinical investigation 2025 · PMID 40111422 · doi:10.1172/JCI184021
This dataset
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.

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 GSE279386_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1172353 and SRA study SRP538314. Searching any of these in the dataset finder brings you back here.

Study design
24 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 24 sample titles: 24 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
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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.