← BioTransfer GEO Dataset Finder
GEO series

Recurrent FGFR-alterations are frequent oncogenic drivers and therapeutic targets in pediatric low-grade gliomas [RNA-Seq 2]

GSE286565 Homo sapiens (human) Single-nucleus RNA-seq 40 samples Submitted 2025/05/16 Platform GPL24676
At a glance

Single-nucleus RNA-seq · human · 40 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
40 samples: 8 conditions, mostly with about 5 replicates eachGroups 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
A diverse landscape of FGFR alterations and co-mutations suggests potential therapeutic strategies in pediatric low-grade gliomasNature communications · 2025
Summary (written by the submitters)
Alterations in Fibroblast growth factor receptor proteins frequently occur as oncogenes in many cancers. FGFR alterations have been reported in a subset of pediatric gliomas, representing a therapeutic target for precision medicine approaches. We performed a genomic analysis of 13,659 gliomas and found that 4.5% harbor FGFR alterations including structural variants and single nucleotide variants.
Read the full summary ↓
FGFR family members are differentially enriched by age, tumor grade, and histological subtype. FGFR1 alterations are most frequent in pediatric gliomas, particularly pediatric low-grade gliomas, while FGFR3 drivers were associated with adult gliomas. In vitro and in vivo functional studies confirm FGFR1 alterations to be sufficient to activate MAPK and mTOR signaling, drive gliomagenesis and activate neuronal transcriptional programs. FGFR1-driven models showed sensitivity to MAPK pathway inhibitors, including panFGFR inhibitors that are FDA approved for use in other cancers. While early FGFR inhibition was sufficient to prolong survival of mice bearing FGFR-driven xenografts, this was insufficient to induce cures. Similarly, review of patients treated with currently available MAPK pathway or FGFR inhibitors revealed modest responses. This study provides key insights into the biology of FGFR1-altered gliomas and potential strategies to therapeutically target them.
Published in
A diverse landscape of FGFR alterations and co-mutations suggests potential therapeutic strategies in pediatric low-grade gliomas
Apfelbaum AA, Morin E, Sturm D et al. · Nature communications 2025 · PMID 40744913 · doi:10.1038/s41467-025-61820-z
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 GSE286565_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1209918 and SRA study SRP556996. Searching any of these in the dataset finder brings you back here.

Study design
8 conditions, mostly with about 5 replicates each
Vector control ×5 PTPN11 E69K alone ×5 FGFR1 N546K ×5 FGFR1 N546K + PTPN11 E69K ×5 TACC1 ×5 FGFR1-ITD ×5 BRAF V600E ×5 BRAF ×5

Supports a between-group comparison across 40 samples.

How we read this: 8 replicated groups read from 40 sample titles; they account for 40 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

Search all human RNA-seq datasets in GEO →

Share this dataset

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.