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GABAergic neuronal lineage development determines clinically actionable targets in diffuse hemispheric glioma, H3G34-mutant [RNA-Seq]

GSE272261 Homo sapiens (human) RNA-seq (gene expression) 57 samples Submitted 2024/08/01 Platform GPL30173
At a glance

RNA-seq (gene expression) · human · 57 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
57 samples: 19 conditions, mostly in triplicateGroups 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
GABAergic neuronal lineage development determines clinically actionable targets in diffuse hemispheric glioma, H3G34-mutantCancer cell · 2024
Summary (written by the submitters)
Diffuse hemispheric gliomas, H3G34R/V-mutant (DHG-H3G34), are uniformly lethal malignancies lacking targeted therapies. These gliomas uniquely originate from interneuronal precursor cells, and leveraging this interneuronal lineage origin for mechanistic and therapeutic insights is a pressing need. Here, we elucidate the cell state composition of DHG-H3G34, resolving a cellular hierarchy along continuous interneuron lineage development.
Read the full summary ↓
In the developing human brain, interneuron lineage precursors form characteristic histological patterns organized in streams of progenitor cells surrounding nests of migratory interneurons, and we find these same spatial patterns to be mirrored in primary DHG-H3G34 tumors. We integrated these findings with genome-wide CRISPR-Cas9 screens, which revealed the majority of dependencies to be genes upregulated in interneuron lineage progenitors, highlighting these as a driver of DHG-H3G34. We validated the essentiality of these lineage-associated targets in patient-derived in vitro and in vivo models. Among these, CDK6 emerged as a targetable dependency, and we demonstrated enhanced sensitivity of DHG-H3G34 tumor cells to CDK4/6 inhibitors and a CDK6-specific degrader. Inhibition of CDK6 shifted cells towards more mature interneuron-like cell states, reduced tumor growth, and prolonged survival of patient-derived xenografts. Concordantly, a patient with progressive DHG-H3G34 under second line therapy was treated with a CDK4/6 inhibitor, and exhibited stable disease for 17 months. Collectively, we identify interneuronal progenitor-like states, organized in characteristic developmental spatial niches, as a distinct vulnerability consistently upregulated in DHG-H3G34 tumors, and provide initial evidence for CDK6 as a clinically actionable target particularly relevant for its interneuronal developmental biology.
Published in
GABAergic neuronal lineage development determines clinically actionable targets in diffuse hemispheric glioma, H3G34-mutant
Liu I, Alencastro Veiga Cruzeiro G, Bjerke L et al. · Cancer cell 2024 · PMID 39232581 · doi:10.1016/j.ccell.2024.08.006
This dataset
Open GSE272261 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 GSE272261_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 PRJNA1135968 and SRA study SRP519960. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39232581) ↗
Study design
19 conditions, mostly in triplicate
BT690_ABE ×3 BT690_CDK6deg ×3 BT690_DMSO_ABE ×3 BT690_DMSO_CDK6deg ×3 BT690_DMSO_RIBO ×3 BT690_RIBO ×3 BT690_shRNA1_CDK6 ×3 BT690_shRNA2_CDK6 ×3 +11 more

Supports a between-group comparison across 57 samples.

How we read this: 19 replicated groups read from 57 sample titles; they account for 57 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

+ 17 more — browse all 57 samples with per-sample file links →

Datasets on a similar topic

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