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Combined epigenetic therapy with FACT and BET inhibitors remodels chromatin and disrupts oncogenic transcription in Diffuse Midline Glioma [Nascent RNA-Seq]

GSE299509 Homo sapiens (human) RNA-seq (gene expression) 30 samples Submitted 2025/12/22 Platform GPL34284
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)
Samples
30 samples: 10 conditions, mostly in triplicateGroups 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
Combined inhibition of FACT and BET disrupts transcription to suppress tumor growth in mouse models of diffuse midline gliomaScience translational medicine · 2026
Summary (written by the submitters)
Aberrant epigenetic regulation is a hallmark of Diffuse Midline Glioma (DMG), an incurable pediatric brain tumor. The H3K27M driver histone mutation leads to transcriptional dysregulation, indicating that targeting the epigenome and transcription may be key therapeutic strategies against this highly aggressive cancer. One such target is the Facilitates Chromatin Transcription (FACT) histone chaperone.
Read the full summary ↓
We found FACT to be enriched at developmental gene promoters, coinciding with regions of open chromatin and binding motifs of core DMG regulatory transcription factors. Furthermore, FACT interacted and co-localized with the Bromodomain and Extra-Terminal Domain (BET) protein BRD4 at promoters and enhancers, suggesting functional cooperation between FACT and BRD4 in DMG. In vitro, a combinatorial therapeutic approach using the FACT inhibitor CBL0137, coupled with BET inhibition revealed potent and synergistic cytotoxicity across a range of DMG cultures. These results were recapitulated in vivo, significantly extending survival in three independent orthotopic PDX models of DMG. Mechanistically, we show that CBL0137 treatment decreased chromatin accessibility, synergizing with BET inhibition to cause broad transcriptional collapse, silencing several key oncogenes including MYC, PDGFRA, MDM4 and SOX2, as well as causing alterations to the splicing landscape. Notably, this combination also elicited immune-related effects, including activation of the interferon response and antigen presentation mechanisms in DMG cells and induction of an activated state in macrophages and T cells, as demonstrated in an immunocompetent setting with spatial transcriptomics. Altogether, our data highlights the therapeutic promise of simultaneously targeting FACT and BET proteins in DMG, offering a dual tumor-intrinsic and immune-mediated strategy for combating this devastating pediatric brain tumor.
Published in
Combined inhibition of FACT and BET disrupts transcription to suppress tumor growth in mouse models of diffuse midline glioma
Holliday H, Khan A, Ehteda A et al. · Science translational medicine 2026 · PMID 41533777 · doi:10.1126/scitranslmed.adr1557
This dataset
Open GSE299509 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 GSE299509_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 PRJNA1274871 and SRA study SRP591341. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41533777) ↗
Study design
10 conditions, mostly in triplicate
DIPG7_RNA_Veh_no4su ×3 DIPG7_RNA_Veh ×3 DIPG7_RNA_CBL0137 ×3 DIPG7_RNA_JQ1 ×3 DIPG7_RNA_Combo ×3 DIPG6_RNA_Veh_no4su ×3 DIPG6_RNA_Veh ×3 DIPG6_RNA_CBL0137 ×3 +2 more

Supports a between-group comparison across 30 samples.

How we read this: 10 replicated groups read from 30 sample titles; they account for 30 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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