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The DUTRENEO trial: bulk prospective biomarker-guided therapy selection and spatial transcriptomic dissection of immunotherapy response in muscle-invasive bladder cancer

GSE328930 Homo sapiens (human) Single-cell RNA-seq 66 samples Submitted 2026/04/24 Platform GPL21697
At a glance

Single-cell RNA-seq · human · 66 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Topic
Bladder cancerThis series is part of our curated briefing on it.
Samples
66 samples
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
Spatial architecture contributes to failure of bulk biomarker-guided neoadjuvant immunotherapy selection in bladder cancer: The DUTRENEO studyCell reports. Medicine · 2026
Summary (written by the submitters)
Predictive biomarkers for immune checkpoint inhibitors (ICI) have largely been identified retrospectively, but their clinical utility remains unproven in prospective settings. Here, we report the results of DUTRENEO, a prospective, randomized phase 2 trial testing whether a retrospective, well-validated, 18-gene tumor inflammation signature (TIS) could guide neoadjuvant ICI therapy in muscle-invasive bladder cancer.
Read the full summary ↓
The trial failed to meet its primary endpoint, demonstrating that bulk gene expression stratification does not sufficiently enrich for responders in this setting. To understand the biological determinants of this failure, we generated a single-cell spatial transcriptomics dataset profiling 377 genes in ~5.4 million cells across large tissue areas. This high-resolution profiling revealed that response to ICI is governed by spatial architectures invisible to bulk assays, specifically the proximity of CD8⁺ T cells to cancer cells and the localized co-expression of checkpoints within epithelial-rich neighborhoods. Furthermore, we identified fibroblast-rich communities as a key feature of immune exclusion in non-responders. We derived a quantitative framework for the design of future clinical trials incorporating spatial analyses, demonstrating that reducing the annotation panel to ≥77 genes and sampling ≥3 mm tissue diameter preserves predictive spatial signal while enabling scalable throughput. These findings elucidate the architectural constraints that limit bulk biomarkers and provide a quantitative framework to embed spatial profiling into clinical studies.
Published in
Spatial architecture contributes to failure of bulk biomarker-guided neoadjuvant immunotherapy selection in bladder cancer: The DUTRENEO study
Grande E, Sibai M, Grases D et al. · Cell reports. Medicine 2026 · PMID 42335902 · doi:10.1016/j.xcrm.2026.102878
This dataset
Open GSE328930 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 GSE328930_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1457045 and SRA study SRP694272. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42335902) ↗
Study design
A cohort of 66 individual samples

The grouping that defines any comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Sample titles run Sample_2702201 … Sample_2719206 and carry no group information.

Samples in this study

+ 26 more — browse all 66 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.