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Alveolar Soft Part Sarcoma is driven by ASPSCR1::TFE3-dependent and therapeutically targetable transcriptional programs [RNA-Seq]

GSE235738 Homo sapiens (human) RNA-seq (gene expression) 35 samples Submitted 2024/04/19 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 35 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
35 samples: 9 conditions, mostly with about 4 replicates eachGroups 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
ASPSCR1::TFE3 Drives Alveolar Soft Part Sarcoma by Inducing Targetable Transcriptional ProgramsCancer research · 2024
Summary (written by the submitters)
Alveolar soft part sarcoma (ASPS) is a rare mesenchymal malignancy driven by the ASPSCR1::TFE3 fusion, though the mechanisms by which this oncogenic transcriptional regulator drives cancer growth are poorly understood. Here, we characterize the transcriptional and chromatin landscapes of ASPS tumors and preclinical models, identifying the essential role of ASPSCR1::TFE3 in tumor cell viability by regulating core transcriptional programs including cell proliferation, angiogenesis, and mitochondrial biology.
Read the full summary ↓
ASPSCR1::TFE3 directly interacts with key epigenetic regulators at enhancers and promoters to support ASPS-associated transcription. Among the effector programs driven by ASPSCR1::TFE3, cell proliferation was driven by high levels of Cyclin D1 expression, and disruption of Cyclin D1/CDK4 signaling led to loss of ASPS proliferative capacity, which when combined with angiogenesis inhibition halted tumor growth in xenografts. These results define the ASPS oncogenic program, mechanisms by which ASPSCR1::TFE3 controls tumor biology, and identify a strategy for therapeutically targeting tumor cell-intrinsic vulnerabilities.
Published in
ASPSCR1::TFE3 Drives Alveolar Soft Part Sarcoma by Inducing Targetable Transcriptional Programs
Sicinska E, Kola VSR, Kerfoot JA et al. · Cancer research 2024 · PMID 38657118 · doi:10.1158/0008-5472.CAN-23-2115
This dataset
Open GSE235738 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 GSE235738_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 PRJNA987537 and SRA study SRP445817. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 38657118) ↗
Study design
9 conditions, mostly with about 4 replicates each
ASPS_Tumo ×5 KYdTAG_0h ×4 KYdTAG_24h ×4 KYdTAG_6h ×4 ASPS-1_DMSO ×4 ASPS-1_Palbo ×4 ASPS_PDX_F ×2 ASPS_PDX_Control ×2 +5 more

Supports a between-group comparison across 31 samples.

How we read this: 9 replicated groups read from 35 sample titles; they account for 31 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
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