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Identifying and Exploiting Combinatorial Synthetic Lethality by Characterizing Adaptive Kinome Rewiring of EGFRvIII-driven Glioblastoma

GSE296151 Mus musculus (mouse) RNA-seq (gene expression) 80 samples Submitted 2025/06/28 Platform GPL19057Platform GPL21626
At a glance

RNA-seq (gene expression) · mouse · 80 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
Mus musculus (mouse)
Samples
80 samples: 80 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.
Summary (written by the submitters)
GBM is an aggressive primary malignant brain tumor that has a poor prognosis. Molecular characterization of GBM has shown that EGFR mutations are present in over 50% of tumors. However, EGFR inhibitors have not shown clinical efficacy in contrast to other EGFR-driven neoplasms due to the unique EGFR biology found in GBM. We believe that upfront combinatorial therapy with EGFR inhibitors can overcome these challenges.
Read the full summary ↓
To identify combinatorial drug targets, we temporally characterized drug induced EGFRvIII kinome rewiring in a cell line with an isogenic genetically engineered GBM EGFRvIII kinome. Kinomic characterization show that kinome rewiring has both shared and unique kinases after acquired resistance develops despite a common genetic origin. Additionally, we noted that the kinases altered in the acute setting are distinct from those in acquired resistance. By identifying these differential temporal kinomic vulnerabilities throughout the drug response process, we generated a kinase signature associated with inhibition of EGFR. Further molecular interrogation of these signature genes reveals that drug treatment induces an unexpected increase in Cdk6 protein but not mRNA despite a decrease in proliferation measured by both live cell counts and transcriptomics. Survival experiments with two cohorts of orthotopic allografts show that upfront combination inhibition of Cdk6, with abemaciclib, and EGFR, with neratinib, significantly prolonged median survival compared to neratinib alone. Our findings suggest that identifying and inhibiting targets with synthetic lethality in the upfront combinatorial setting is a viable approach for precision oncology and may help provide an avenue to overcome the resistance mechanisms that contributed to the failures of EGFR as a molecular target in GBM.
This dataset
Open GSE296151 in finder → Find more mouse 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 GSE296151_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 PRJNA1257934 and SRA study SRP582699. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
80 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 80 sample titles: 80 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

+ 40 more — browse all 80 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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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.