GEO series

Bromodomain Inhibitors to Prevent Development of Resistance to Targeted Kinase Inhibitors in Schwannomas [RNA-seq]

GSE302434 Homo sapiens (human) RNA-seq (gene expression) 43 samples Submitted 2026/07/11 Platform GPL24676

At a glance

RNA-seq (gene expression) · human · 43 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: 43 samples: 42 distinct titles, 1 of them repeatedGroups 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.

Summary (written by the submitters)

Neurofibromatosis type 2 (NF2) is an inherited disorder that promotes tumor growth on nerves throughout the nervous system. NF2 affects around 1 in 33,000 individuals worldwide and is caused by loss of function of the merlin tumor suppressor encoded by the NF2 gene. Tumor control early in life is critical for maintaining quality of life.

Read the full summary ↓

Currently, treatments available for NF2-associated tumors are surgery and radiation therapy. Off-label use of cancer drugs are being tried, as there are no Food and Drug Administration (FDA)-approved drug therapies for NF2. As a member of the SYNODOS for NF2 consortium, we performed unbiased drug combination screening using schwannoma model cell lines. We identified a BRD4 and a PAK inhibitor that acted in concert to selectively slow growth of a human schwannoma line compared to its merlin-expressing parental line. BRD4 is a member of the BET family of bromodomain containing proteins that bind acetylated lysine in transcription regulatory proteins to alter gene expression. BRD proteins contribute to development of resistance to cancer drugs. Follow-up studies confirmed the drugs’ interaction in multiple mouse and human schwannoma lines. We will extend this research by assessing the ability of BRD4 inhibitors in combination with multiple PAK, PI3K, MEK, and FAK inhibitors, some of which are in NF2 clinical trials (selumetinib, crizotinib, brigatinib, trametinib) to reduce the viability of schwannoma lines. We will measure whether the combinations provide long-lasting inhibition of cell proliferation, induce cell death, and block compensatory escape pathways that often arise when cells are treated with a single kinase inhibitor. We will use an established mouse xenograft model to test the effectiveness of the best performing in vitro drug combination. Mechanisms of action studies will complete the preclinical evaluation of the advanced drug combination in a representative cell line. Our proposed study is timely because the results will indicate whether drug resistance could develop for several of the kinase inhibitors in ongoing NF2 clinical trials. Moreover, the results could indicate whether BRD4 inhibitors could serve as an adjuvant therapy to prevent drug resistance. By assessing the RNAome and methylome of single and dual treated human schwannoma cells, we will uncover the genes and RNAs that undergo BRD4-dependent epigenetic regulation to trigger compensatory pathways. With further validation, these candidate genes and RNAs may serve as biomarkers for treatment response and/or resistance development. Thus, these preclinical studies are at the forefront of addressing questions critical to ongoing efforts to develop a pharmacotherapy for NF2 schwannomas.

This dataset

Open GSE302434 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 GSE302434_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 PRJNA1290928 and SRA study SRP600515. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Study design

42 distinct titles, 1 of them repeated

HS05 DMSO set I ×2

How we read this: Read from 43 sample titles: 42 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

GSM9103915 HS02 DMSO set A
GSM9103916 HS02 Briga set A
GSM9103917 HS02 Fime set A
GSM9103918 HS02 BMS set A
GSM9103919 HS02 Briga + BMS set A
GSM9103920 HS02 DMSO set B
GSM9103921 HS02 Briga set B
GSM9103922 HS02 Fime set B
GSM9103923 HS02 BMS set B
GSM9103924 HS05 BMS set C
GSM9103925 HS05 Briga + BMS set C
GSM9103926 HS05 DMSO set D
GSM9103927 HS05 Briga set D
GSM9103928 HS05 Fime set D
GSM9103929 HS05 BMS set D
GSM9103930 HS05 Briga + BMS set D
GSM9103931 HS02 DMSO set E
GSM9103932 HS02 Briga set E
GSM9103933 HS02 Fime set E
GSM9103934 HS02 BMS set E
GSM9103935 HS02 Briga + BMS set E
GSM9103936 HS02 DMSO set F
GSM9103937 HS02 Briga set F
GSM9103938 HS02 Fime set F
GSM9103939 HS02 BMS set F
GSM9103940 HS02 Briga + BMS set F
GSM9103941 HS05 DMSO set G
GSM9103942 HS05 Briga set G
GSM9103943 HS05 Fime set G
GSM9103944 HS05 BMS set G
GSM9103945 HS05 Briga + BMS set G
GSM9103946 HS05 Briga set H
GSM9103947 HS05 Fime set H
GSM9103948 HS05 BMS set H
GSM9103949 HS05 Briga + BMS set H
GSM9103950 NCH1 chunk A
GSM9103951 NCH1 chunk C
GSM9103952 NCH1 chunk F
GSM9103953 NCH1 chunk M
GSM9103954 HS05 DMSO set I 1

+ 3 more — browse all 43 samples with per-sample file links →

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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.