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The XPO7-NPAT axis represents key vulnerabilities in TP53-mutated acute myeloid leukemia (ATAC-Seq)

GSE264594 Mus musculus (mouse) ChIP-seq / ATAC-seq (genome binding and open chromatin) 9 samples Submitted 2026/04/22 Platform GPL21626
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · mouse · 9 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Mus musculus (mouse)
Samples
9 samples: 3 × NpatKI, dTAG, 0hr, vs 3 × NpatKI, dTAG, 3hr, vs 3 × NpatKI, dTAG, 12hr,Groups 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.
Summary (written by the submitters)
Mutation of TP53, a tumor suppressor in cancer, is common and leads to extremely poor prognosis. To identify vulnerabilities in TP53-mutated tumors, we performed genome-wide CRISPR/Cas9 screens using isogenic Trp53 wild-type and knockout mouse acute myeloid leukemia (AML) lines. Here, we show that histone gene regulation governed by the XPO7-NPAT pathway is essential for survival of TP53-mutated AML cells.
Read the full summary ↓
In TP53 wild-type cells, XPO7 enhances p53 nuclear localization and functions as a tumor suppressor, but in TP53-mutated cells, XPO7 promotes cell proliferation by retaining NPAT, a histone gene activator, in the nucleus. NPAT depletion led to genome-wide histone loss, enhancing vulnerability to genotoxic stress. Human AML cases show predominant expression of XPO7 and NPAT when TP53 is mutated, suggesting a potential therapeutic vulnerability.
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 GSE264594_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1103209 and SRA study SRP503402. Searching any of these in the dataset finder brings you back here.

Study design
3 × NpatKI, dTAG, 0hr, vs 3 × NpatKI, dTAG, 3hr, vs 3 × NpatKI, dTAG, 12hr,

Supports a between-group comparison across 9 samples.

How we read this: 3 replicated groups read from 9 sample titles; they account for 9 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
Datasets on a similar topic

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