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A pilot trial of perioperative IDH Inhibition in treatment-naïve IDH-mutant glioma

GSE292732 Homo sapiens (human) RNA-seq (gene expression) 11 samples Submitted 2025/06/02 Platform GPL34281
At a glance

RNA-seq (gene expression) · human · 11 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
Lower-grade gliomaThis series is part of our curated briefing on it.
Samples
11 samples: 11 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
Perioperative IDH inhibition in treatment-naive IDH-mutant glioma: a pilot trialNature medicine · 2025
Summary (written by the submitters)
Mutant-IDH (mIDH) inhibition significantly improves progression free survival in patients with mIDH WHO grade 2 glioma, however almost all patients will progress, and there is a lack of understanding on the mechanisms of adaptation to mIDH inhibition. In the first pre- and post-treatment perioperative trial in patients with mIDH1 low-grade glioma, we tested safusidenib (AB-218 / DS-1001b), an oral, blood brain barrier penetrant IDH1 R132X inhibitor, in a single arm, open label study.
Read the full summary ↓
The primary endpoint demonstrated the feasibility and acceptability of conducting a two-stage perioperative trial and treatment was well tolerated, with ongoing follow-up for safety and efficacy. Tumor 2-hydroxyglutarate quantification revealed on-target activity, resulting in significant alterations in differentiation programs and neural excitability, functionally validated by patch clamp electrophysiology. Taken together, these results provide a detailed understanding on mechanisms of mIDH inhibition in glioma and the safety and feasibility of this perioperative approach, which can be applied broadly in clinical trial design, advancing drug development in glioma. Funded by AnHeart Therapeutics (a Nuvation Company) and the Victorian Government; ClinicalTrials.gov number NCT05577416.
Published in
Perioperative IDH inhibition in treatment-naive IDH-mutant glioma: a pilot trial
Drummond KJ, Spiteri M, Cain SA et al. · Nature medicine 2025 · PMID 40841487 · doi:10.1038/s41591-025-03884-4
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 GSE292732_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 PRJNA1241178 and SRA study SRP572661. Searching any of these in the dataset finder brings you back here.

Study design
11 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 11 sample titles: 11 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
More Lower-grade glioma datasets

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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.