GEO series

Elucidating and Pharmacologic Targeting of Coexisting Diffuse Midline Subpopulation Dependencies

GSE262775 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2025/12/22 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 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: Homo sapiens (human)
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: Systematic design of combination therapy by targeting master regulators of coexisting diffuse midline glioma cell statesNature genetics · 2026

Summary (written by the submitters)

Diffuse Midline Glioma (DMG) are universally fatal, primarily pediatric malignancies affecting the midline structures of the central nervous system. Despite decades of clinical trials, their treatment remains limited to palliative radiation therapy. A major challenge is the coexistence of molecularly distinct malignant cell states with potential orthogonal drug sensitivities.

Read the full summary ↓

To address it, we leveraged established network-based methodologies to elucidate Master Regulator (MR) proteins representing mechanistic dependencies of seven coexisting subpopulations identified from tumor biopsies. We then leveraged perturbational profiles of 372 clinically relevant drugs to identify those capable of inverting the activity of subpopulation specific MRs and validated their specificity in vivo. While individual drugs predicted by the analysis to target individual subpopulations—such as avapritinib, larotrectinib, and ruxolitinib—produced only modest tumor growth reduction in orthotopic allograft models, they systemic co-administration induced highly significant survival extension.

Published in

Systematic design of combination therapy by targeting master regulators of coexisting diffuse midline glioma cell states

Calvo Fernández E, Tomassoni L, Zhang X et al. · Nature genetics 2026 · PMID 42020604 · doi:10.1038/s41588-026-02550-w

This dataset

Open GSE262775 in finder → Find more human RNA-seq datasets →

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.

Series matrix ↓

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 GSE262775_RAW.tar (it can run to tens of gigabytes).

All supplementary files ↗ Whole study · RAW.tar ↓

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.

Raw reads on ENA ↗

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 PRJNA1093349 and SRA study SRP498607. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42020604) ↗

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

GSM8178733 Control, JE003, scRNASeq
GSM8178734 Control, JE004, scRNASeq
GSM8178735 Dinaciclib, JE008, scRNASeq
GSM8178736 Etoposide, JE009, scRNASeq
GSM8178737 Larotrectinib, JE011, scRNASeq
GSM8178738 Venetoclax, JE012, scRNASeq
GSM8178739 Trametinib, JE013, scRNASeq
GSM8178740 Control, JE014, scRNASeq
GSM8178741 Napabucasin, JE015, scRNASeq
GSM8178742 Ruxolitinib, JE016, scRNASeq
GSM8178743 Avapritinib, JE017, scRNASeq
GSM8178744 Larotrectinib, JE018, scRNASeq
GSM8178745 Etoposide, JE020, scRNASeq
GSM8178746 Napabucasin, JE021, scRNASeq
GSM8178747 Mocetinostat, JE023, scRNASeq
GSM8178748 Mocetinostat, JE024, scRNASeq
GSM8178749 Ruxolitinib, JE024, scRNASeq
GSM8178750 Venetoclax, JE025, scRNASeq
GSM8178751 Dinaciclib, JE026, scRNASeq
GSM8178752 Venetoclax, JE027, scRNASeq
GSM8178753 Avapritinib, JE028, scRNASeq
GSM8178754 Napabucasin, JE029, scRNASeq
GSM8178755 Trametinib, TR002, scRNASeq
GSM8178756 Control, VH005, scRNASeq

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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.