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DeCAF redefines fibroblast states uncovering multidimensional tumor-stroma relationships driving clinical tumor progression and immunotherapy response [RNA-Seq]

GSE310957 Homo sapiens (human) Single-cell RNA-seq 129 samples Submitted 2025/12/18 Platform GPL24676Platform GPL18573
At a glance

Single-cell RNA-seq · human · 129 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
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
129 samples: 129 × PDAC, Human Tumor, sampleGroups 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
DeCAF defines clinical fibroblast subtypes and multidimensional tumor-stroma crosstalk shaping prognosis and immunotherapy responseCell reports. Medicine · 2026
Summary (written by the submitters)
Cancer-associated fibroblast (CAF) heterogeneity within pancreatic ductal adenocarcinoma (PDAC) has been previously assessed through single-cell RNA sequencing (scRNAseq) to discover distinct CAF populations such as myCAFs and iCAFs. While useful as a biological framework, no studies have conclusively and robustly demonstrated a correlation of CAF subpopulations with clinical prognosis or therapy response.
Read the full summary ↓
We define restraining (rest) and promoting (pro) CAFs in patient samples that are both prognostic and predictive of therapy response in disparate tumor types. Using a single sample classifier, DeCAF, we uncover distinct clinical and spatial interactions between pro- and restCAF subtypes and basal-like and classical tumor subtypes that support tumor stroma crosstalk where basal-like tumors are enriched in proCAFs and classical tumors are enriched in restCAFs. Finally, we find striking differences in the immune contexture of pro- and restCAF tumors where restCAF dominant tumors are enriched in patients who respond to immune checkpoint inhibition (ICI) in two large clinical trials, and proCAF dominant tumors are enriched in patients who respond to myeloid inhibition. This work is the first to define CAF subtypes that are clinically robust, prognostic, predictive of immunotherapy response, and associate with tumor molecular subtype.
Published in
DeCAF defines clinical fibroblast subtypes and multidimensional tumor-stroma crosstalk shaping prognosis and immunotherapy response
Peng XL, McCabe IC, Kharitonova EV et al. · Cell reports. Medicine 2026 · PMID 41707654 · doi:10.1016/j.xcrm.2026.102611
This dataset
Open GSE310957 in finder → Find more human RNA-seq datasets → Pancreatic 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 GSE310957_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 PRJNA1367602. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41707654) ↗
Study design
129 × PDAC, Human Tumor, sample

Supports a between-group comparison across 129 samples.

How we read this: 1 replicated groups read from 129 sample titles; they account for 129 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

+ 89 more — browse all 129 samples with per-sample file links →

More Pancreatic 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.