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Distinct transcriptional and functional modules mediate resistance to targeted therapy in glioblastoma

GSE319338 Homo sapiens (human) Single-cell RNA-seq 13 samples Submitted 2026/07/27 Platform GPL34328Platform GPL34281
At a glance

Single-cell RNA-seq · human · 13 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. 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)
Topic
GlioblastomaThis series is part of our curated briefing on it.
Samples
13 samples: 4 × Perturb-seq in SB28 cells treated with vehicle/selumetinib/RMC-4550, 10x vs 3 × GBM43 intracranial xenografts, vehicle control, sample vs 3 × GBM43 intracranial xenografts, selumetinib treated, sample vs 3 × GBM43 intracranial xenografts, RMC-4550 treated, sampleGroups 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)
Glioblastomas (GBM) harbor recurrent mutations in receptor tyrosine kinase (RTK) signaling, yet targeted therapy has shown limited efficacy. To dissect mechanisms underlying drug resistance, we combined single cell RNA-sequencing (scRNA-seq) and functional genomic screens following MEK or SHP2 inhibition in preclinical GBM models. ScRNA-seq of mouse intracranial xenografts revealed that although mesenchymal-like GBM subpopulation exhibits heightened sensitivity to both MEK or SHP2 inhibition, each targeted therapy induces distinct transcriptional signatures.
Read the full summary ↓
Genome-wide CRISPR interference (CRISPRi) screens identified shared mediators of MEK or SHP2 inhibitor sensitivity enriched for the conserved downstream effector Ras/RAF/MEK pathway. However, genes uniquely mediating SHP2 inhibitor response were enriched for Ras/RAF/MEK-independent signaling modules such as SRC family kinases, GSK3B, and SMAD proteins regulating cell adhesion and differentiation. Perturb-seq of 39 target genes following MEK or SHP2 inhibition identified 10 discrete clusters with distinct effects on Ras signaling and cellular phenotypes. Proliferation and differentiation gene expression signatures clustered genetic perturbations into 4 broad classes: cell cycle regulators, positive effectors of Ras/RAF/MEK signaling, negative regulators of Ras/RAF/MEK signaling, or parallel regulators of Ras/RAF/MEK independent pathways. Combinatorial linear modeling from transcriptional signatures identified multiple gene-drug relationships including expected synergistic and potentiation combinations between MEK, SHP2, and CDKN2A/B and unexpected antagonistic and neomorphic interactions with indirect effectors such as LZTR1 and RAC1. Finally, preclinical testing of predicted synergistic inhibitor combinations with MEK, SHP2, and CDK4/6 inhibition demonstrated improved efficacy compared to molecular monotherapy in multiple GBM preclinical models. Taken together, our approach provides a systems-level framework for understanding RTK-Ras signaling network principles and targeted therapy resistance in glioblastoma.
This dataset
Open GSE319338 in finder → Find more RNA-seq datasets → Glioblastoma landscape →
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 GSE319338_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: 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 PRJNA1423048. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
4 × Perturb-seq in SB28 cells treated with vehicle/selumetinib/RMC-4550, 10x vs 3 × GBM43 intracranial xenografts, vehicle control, sample vs 3 × GBM43 intracranial xenografts, selumetinib treated, sample vs 3 × GBM43 intracranial xenografts, RMC-4550 treated, sample

Supports a case/control comparison: 6 samples read as cases, 3 as controls.

How we read this: 4 replicated groups read from 13 sample titles; they account for 13 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
More Glioblastoma datasets

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