GEO series

Cellular states associated with metastatic organotropism and survival in patients with pancreatic ductal adenocarcinoma

GSE253429 Homo sapiens (human) Single-nucleus RNA-seq 21 samples Submitted 2025/07/16 Platform GPL24676

At a glance

Single-nucleus RNA-seq · human · 21 samples

What it is: Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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: 21 samples: 21 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: Cellular states associated with metastatic organotropism and survival in patients with pancreatic ductal adenocarcinomaNature genetics · 2025

Summary (written by the submitters)

Some patients with localized pancreatic ductal adenocarcinoma (PDAC) undergo resection with curative intent, but most experience disease recurrence and succumb to their disease. Through examination of 743 PDAC patients in a single-center we find that the site of first recurrence is a major predictor of survival. Patients with initial liver-metastatic recurrence (LIV-MR, n = 100) had significantly shorter disease-free and overall survival compared to those with initial recurrence to the lung (LUN-MR, n = 31).

Read the full summary ↓

We performed single-nucleus RNA-seq (snRNA-seq) and low-pass whole-genome sequencing (lpWGS) on a representative population of this cohort, including 21 primary PDACs with either LIV-MR or LUN-MR, and found that malignant cells adopt transcriptional programs that mimicked that of their ultimate metastatic target organ. We validated the LIV-MR and LUN-MR signatures in multiple data sets, including single-cell RNA-seq of normal human adult and fetal liver and lung tissues, bulk, single-cell, and spatial transcriptomics of PDAC lung and liver metastases, and in vivo data modelling metastatic organotropism in PDAC and other lineages. We reconstructed two different trajectories of malignant transformation in PDAC and find that the LIV-MR and LUN-MR are associated with these, suggesting that subsequent metastatic organotropism may be determined early in tumorigenesis. Furthermore, primary PDAC tumors recurring with LIV-MR compared to LUN-MR were deplete of infiltrating T cells and enriched with M2-like macrophages, highlighting potential contributions to subsequent outcomes. Taken together, this work provides evidence for pre-existing cellular adaptations that determine metastatic organotropism and may be useful for prognostication and development of therapeutic strategies at the time of diagnosis of resectable PDAC.

Published in

Cellular states associated with metastatic organotropism and survival in patients with pancreatic ductal adenocarcinoma

Chalabi Hajkarim M, May M, Amin AD et al. · Nature genetics 2025 · PMID 41145870 · doi:10.1038/s41588-025-02345-5

This dataset

Open GSE253429 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 GSE253429_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 PRJNA1065782. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41145870) ↗

Study design

21 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 21 sample titles: 21 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

GSM8020617 PDAC-liver, sample 1, snRNAseq [PN1]
GSM8020618 PDAC-liver, sample 2, snRNAseq [PN10]
GSM8020619 PDAC-liver, sample 3, snRNAseq [PN18]
GSM8020620 PDAC-liver, sample 4, snRNAseq [PN23]
GSM8020621 PDAC-liver, sample 5, snRNAseq [PN27]
GSM8020622 PDAC-liver, sample 6, snRNAseq [PN3]
GSM8020623 PDAC-liver, sample 7, snRNAseq [PN8]
GSM8020624 PDAC-liver, sample 8, snRNAseq [PN9]
GSM8020625 PDAC-lung, sample 9, snRNAseq [PN13]
GSM8020626 PDAC-lung, sample 10, snRNAseq [PN14]
GSM8020627 PDAC-lung, sample 11, snRNAseq [PN15]
GSM8020628 PDAC-lung, sample 12, snRNAseq [PN16]
GSM8020629 PDAC-lung, sample 13, snRNAseq [PN17]
GSM8020630 PDAC-lung, sample 14, snRNAseq [PN19]
GSM8020631 PDAC-lung, sample 15, snRNAseq [PN2]
GSM8020632 PDAC-lung, sample 16, snRNAseq [PN24]
GSM8020633 PDAC-lung, sample 17, snRNAseq [PN28]
GSM8020634 PDAC-lung, sample 18, snRNAseq [PN4]
GSM8020635 PDAC-lung, sample 19, snRNAseq [PN5]
GSM8020636 PDAC-lung, sample 20, snRNAseq [PN6]
GSM8020637 PDAC-lung, sample 21, snRNAseq [PN7]

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GSE135731 Genome-wide identification of circular RNAs with critical roles in pancreatic ductal adenocarcinoma 3 samples same disease briefing · shares adenocarcinoma, pancreatic, ductal
GSE71729 Virtual Microdissection of Pancreatic Ductal Adenocarcinoma Reveals Tumor and Stroma Subtypes. 357 samples same disease briefing · shares adenocarcinoma, pancreatic, ductal
GSE102238 Gene expression signatures associated with Perineural Invasion in pancreatic ductal adenocarcinoma 100 samples same disease briefing · shares adenocarcinoma, pancreatic, ductal
GSE118694 Application of the High-throughput TAB-Array for the Discovery of Novel 5-Hydroxymethylcytosine Biomarkers in Pancreatic Ductal Adenocarcinoma 34 samples same disease briefing · shares adenocarcinoma, pancreatic, ductal
GSE125179 microRNA modulated networks of adaptive and innate immune response in pancreatic ductal adenocarcinoma 28 samples same disease briefing · shares adenocarcinoma, pancreatic, ductal
GSE114778 Plasma miRNA expression profiling of pancreatic ductal adenocarcinoma patient, chronic pancreatitis patients and healthy population in China 15 samples same disease briefing · shares adenocarcinoma, pancreatic, ductal

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