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Dissecting Resistance to CDK4/6 Inhibition in Experimental Glioma via Genome-wide CRISPR-Cas9 Screening

GSE303100 Homo sapiens (human) RNA-seq (gene expression) 24 samples Submitted 2026/06/22 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
Molecular modulators of cyclin-dependent kinase 4/6 inhibitor response in experimental glioma identified through genome-wide CRISPR-Cas9 screeningNeuro-oncology · 2026
Summary (written by the submitters)
CDK4/6 inhibitors exhibit therapeutic potential in several tumor entities. Their clinical efficacy in glioblastoma might be limited by intrinsic and acquired resistance mechanisms. Understanding underlying genetic and molecular factors of this resistance might inform the design of effective therapeutic strategies.
Read the full summary ↓
Genome-wide CRISPR-Cas9 screens offer a powerful approach to systematically identify resistance drivers and synthetic lethal interactions, paving the way for rational combination therapies.We conducted genome-wide CRISPR-Cas9 knockout and activation screens in human glioma cell lines LN229 and GS9 under CDK4/6 inhibition using Brunello and Calabrese libraries. Top hits were validated using functional assays. Furthermore, we conducted bulk RNA sequencing of treated and untreated cells for transcriptomic insights into drug response modifiers and molecular pathways involved.Functional genomics identified Ambra1 and CCNE1 as key drivers of resistance. Both Ambra1 knockout and CCNE1 overexpression, conferred resistance across both long-term and stem-like glioma models. Additionally, knockout of CHEK1 and FAM122A uncovered synthetic lethal interactions with CDK4/6 inhibition. Functional validation confirmed that glioma cells rely on CHEK1 or FAM122A for cell cycle progression, with cell line specific sensitivity. Transcriptomic analyses consistently highlighted alterations in pathways associated with cell cycle regulation, DNA replication, and the Fanconi anemia pathway. Of note, several differentially expressed genes overlapped with screen hits, supporting the robustness of our findings. Notably, pharmacological inhibition of CHEK1, both in vitro and ex vivo, enhanced sensitivity to CDK4/6 inhibition in a sequence-dependent manner, indicating a critical role of the sequence of drug administration.
Published in
Molecular modulators of cyclin-dependent kinase 4/6 inhibitor response in experimental glioma identified through genome-wide CRISPR-Cas9 screening
Surender S, Haeusser LA, Kuhlburger L et al. · Neuro-oncology 2026 · PMID 42083788 · doi:10.1093/neuonc/noag093
This dataset
Open GSE303100 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 GSE303100_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 PRJNA1294172 and SRA study SRP602336. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42083788) ↗
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
Datasets on a similar topic

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