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An in vitro pharmacogenomic approach reveals subtype-specific therapeutic vulnerabilities in atypical teratoid/rhabdoid tumors (AT/RT) [RNASeq]

GSE282972 Homo sapiens (human) RNA-seq (gene expression) 13 samples Submitted 2025/02/26 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 13 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
13 samples: 9 distinct titles, 2 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)
Atypical teratoid/rhabdoid tumor (AT/RT) is a highly malignant embryonal brain tumor driven by genetic alterations inactivating the SMARCB1 or, less commonly, the SMARCA4 gene. Large-scale molecular profiling studies have identified distinct molecular subtypes termed AT/RT-TYR, -SHH and -MYC. Despite the increasing knowledge of AT/RT biology, curative treatment options are still lacking for certain risk groups and outcomes of these patients remain poor.
Read the full summary ↓
We performed an in vitro high-throughput drug screen of 768 small molecule drugs covering conventional chemotherapeutic agents and late-stage developmental drugs in 13 AT/RT cell lines and determined intra- and inter-entity differential responses to unravel specific vulnerabilities. Our data demonstrated in vitro preferential activity of mitogen-activated protein kinase kinase (MEK) and mouse double minute 2 homolog (MDM2) inhibitors in AT/RT cell lines compared to other high-grade brain tumor cell lines including medulloblastoma and malignant glioma models. Moreover, we were able to link distinct drug response patterns to AT/RT molecular subtypes through integration of drug response data with large-scale DNA methylation and RNASeq-based expression profiles. Subtype-dependent drug response profiles demonstrated sensitivity of AT/RT-SHH cell lines to B-cell lymphoma 2 (BCL2) and heat shock protein 90 (HSP90) inhibitors, and increased activity of microtubule inhibitors, kinesin spindle protein (KSP) inhibitors, and the eukaryotic translation initiation factor 4E (eIF4E) inhibitor briciclib in a subset of AT/RT-MYC cell lines. In summary, our in vitro pharmacogenomic approach revealed preclinical evidence of tumor type- and subtype-specific therapeutic vulnerabilities in AT/RT cell lines that may inform future in vivo and clinical evaluations of novel pharmacological strategies.
This dataset
Open GSE282972 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 GSE282972_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 PRJNA1191390 and SRA study SRP547957. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
9 distinct titles, 2 of them repeated
CHLA ×4 ATRT ×2

How we read this: Read from 13 sample titles: 9 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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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.