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Spatially resolved subtype progression reveals metabolic vulnerabilities in pancreatic ductal adenocarcinoma II

GSE309739 Homo sapiens (human) RNA-seq (gene expression) 16 samples Submitted 2026/04/22 Platform GPL24676
At a glance

RNA-seq (gene expression) · human · 16 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)
Topic
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
16 samples: 16 samples, each with a distinct title — no replicated groupsGroups 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
Spatially-resolved subtype progression reveals metabolic vulnerabilities in pancreatic ductal adenocarcinomaMolecular cancer · 2026
Summary (written by the submitters)
Pancreatic ductal adenocarcinoma (PDAC) exhibits profound molecular heterogeneity and poor prognosis, necessitating novel tailored therapies. The basal and classical subtypes - driven by glycolysis versus lipid metabolism - have distinct prognostic implications. We mapped PDAC molecular subtype heterogeneity, capturing spatially-resolved gene expression signatures and generating a comprehensive high-resolution dataset of 42,035 spatial spots.
Read the full summary ↓
Subtype assignments were validated via multiplex immunofluorescence and quantitative analyses in patient-derived organoids. Our analysis resolved cancer cell signatures, deconvoluted intra-tumoral heterogeneity, and delineated a classical-to-basal trajectory. We identified metabolically ‘hot’, high-grade tumor niches characterized by concurrent enrichment of glycolysis and lipogenesis across both subtypes, nominating them as subtype-agnostic therapeutic targets. Preclinical models demonstrated that despite the basal subtype’s glycolysis dependence, both classical and basal tumors are susceptible to glycolysis inhibition. This work challenges the dogma of subtype-specific therapeutic silos and demonstrates highly adaptable energetic niches as reservoirs to drive tumor progression.
Published in
Spatially-resolved subtype progression reveals metabolic vulnerabilities in pancreatic ductal adenocarcinoma
Schmitt AM, Gätje FB, Tran MYR et al. · Molecular cancer 2026 · PMID 41896850 · doi:10.1186/s12943-026-02628-3
This dataset
Open GSE309739 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 GSE309739_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 PRJNA1336837. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41896850) ↗
Study design
16 samples, each with a distinct title — no replicated groups

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

How we read this: Read from 16 sample titles: 16 distinct titles with little repetition. 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
More Pancreatic cancer datasets

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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.