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Integrative analysis of patient-derived tumoroids and ex vivo organoid modeling of ARID1A loss in bladder cancer reveals therapeutic molecular targets

GSE271565 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 46 samples Submitted 2024/11/07 Platform GPL17303Platform GPL28038Platform GPL21697
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 46 samples

What it is
ChIP-seq / ATAC-seq (genome binding and open chromatin)Where proteins bind the genome, or which regions are open and active.
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
46 samples: 7 conditions, mostly in duplicateGroups 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
Integrative analysis of patient-derived tumoroids and ex vivo organoid modelling of ARID1A loss in bladder cancer reveals therapeutic molecular targetsCancer letters · 2025
Summary (written by the submitters)
Somatic mutations in ARID1A (AT-rich interactive domain-containing protein 1A) are present in approximately 25% of bladder cancers (BC) and are associated with poor prognosis. With a view to discover effective treatment options for ARID1A-deficient BC patients, we set out to identify targetable effectors dysregulated consequent to ARID1A deficiency.
Read the full summary ↓
Integrative analyses of ARID1A depletion in normal organoids and data mining in publicly available datasets revealed upregulation of DNA repair and cell cycle-associated genes consequent to loss of ARID1A and identified CHEK1 (Checkpoint kinase 1) and chromosomal passenger complex member BIRC5 (Baculoviral IAP Repeat Containing 5) as therapeutically drug-able candidate molecular effectors. Ex vivo treatment of patient-derived BC tumoroids with clinically advanced small molecule inhibitors targeting CHEK1 or BIRC5 was associated with increased DNA damage signalling and apoptosis, and selectively induced cell death in tumoroids lacking ARID1A protein expression. Thus, integrating public datasets with patient-derived organoid modelling and ex-vivo drug testing can uncover key molecular effectors and mechanisms of oncogenic transformation, potentially leading to novel therapeutic strategies. Our data point to ARID1A protein expression as a suitable candidate biomarker for the selection of BC patients responsive to therapies targeting BIRC5 and CHEK1.
Published in
Integrative analysis of patient-derived tumoroids and ex vivo organoid modelling of ARID1A loss in bladder cancer reveals therapeutic molecular targets
Scholtes MP, Akbarzadeh M, Galaras A et al. · Cancer letters 2025 · PMID 39892702 · doi:10.1016/j.canlet.2025.217506
This dataset
Open GSE271565 in finder → Find more human RNA-seq datasets → Bladder cancer 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 GSE271565_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: Look for peak files (.bed, .narrowPeak) and signal tracks (.bigWig) among the supplementary files; the raw reads are on ENA.

Also filed as BioProject PRJNA1132228 and SRA study SRP518175. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39892702) ↗
Study design
7 conditions, mostly in duplicate
PDT ×21 NO ×5 PDT10 ×2 PDT19 ×2 Ctrl ×2 ShARID1A ×2 ShE2F8 ×2 NO8 shCTRL ×1 +9 more

Supports a between-group comparison across 36 samples.

How we read this: 7 replicated groups read from 46 sample titles; they account for 36 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

+ 6 more — browse all 46 samples with per-sample file links →

More Bladder cancer datasets

Search all human RNA-seq datasets in GEO →

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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.