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Chemoresistance of TP53 mutant AML requires the mevalonate byproduct, GGPP, for induction of an adaptive stress response

GSE296181 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2025/08/25 Platform GPL16791
At a glance

RNA-seq (gene expression) · human · 30 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)
Topic
Acute myeloid leukemiaThis series is part of our curated briefing on it.
Samples
30 samples: 30 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
Chemoresistance of TP53 mutant acute myeloid leukemia requires the mevalonate byproduct, geranylgeranyl pyrophosphate, for induction of an adaptive stress responseLeukemia · 2025
Summary (written by the submitters)
Acute myeloid leukemia with mutations in TP53 (TP53mut AML) is fatal with a median survival of 6 months. RNA sequencing on purified AML patient samples showed that TP53mut AML had higher expression of mevalonate pathway genes. Using novel, isogenic TP53mut AML cell lines and primary samples, we determined that TP53mut AML resistance to AML chemotherapy cytarabine (AraC) correlated with increased mevalonate pathway activity, a lower induction of reactive oxygen species (ROS), and a mitochondrial response with increased mitochondrial mass and oxidative phosphorylation.
Read the full summary ↓
Pretreatment with the statin class of mevalonate pathway inhibitors reversed these effects and chemosensitized TP53mut AML. The geranylgeranyl pyrophosphate (GGPP) branch of the mevalonate pathway was required for TP53mut AML chemoresistance. In addition to its role in mitochondria biogenesis, we identified a novel function of GGPP in regulating one-carbon metabolism and subsequent glutathione and polyamine synthesis for management of AraC-induced ROS. However, statins alone were inadequate to fully reverse chemoresistance in vivo and in a retrospective study of 364 TP53mut AML patients who received chemotherapy concurrently with a statin. Finally, we identified clinical settings and strategies to successfully target the mevalonate pathway, particularly to address the unmet need of TP53mut AML
Published in
Chemoresistance of TP53 mutant acute myeloid leukemia requires the mevalonate byproduct, geranylgeranyl pyrophosphate, for induction of an adaptive stress response
Skuli SJ, Bakayoko A, Kruidenier M et al. · Leukemia 2025 · PMID 40634510 · doi:10.1038/s41375-025-02668-6
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 GSE296181_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 PRJNA1258162 and SRA study SRP582768. Searching any of these in the dataset finder brings you back here.

Study design
30 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 30 sample titles: 30 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.