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Multiomic sequencing identifies myeloid cell associations with neoadjuvant chemotherapy treatment response in pancreatic adenocarcinoma (PDAC)

GSE300595 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 24 samples Submitted 2026/06/24 Platform GPL30173
At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 24 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
Pancreatic cancerThis series is part of our curated briefing on it.
Samples
24 samples: 24 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.
Summary (written by the submitters)
Pancreatic ductal adenocarcinoma (PDAC) is among the deadliest human malignancies with poor prognosis and limited therapeutic success. Despite significant advancements in therapeutic strategies over recent decades, the development of effective treatments for pancreatic ductal adenocarcinoma (PDAC) remains a major challenge due to its complex tumor microenvironment (TME).
Read the full summary ↓
The PDAC TME is characterized by tumor-infiltrating immune cells, immunosuppressive cells, and cancer-associated fibroblasts (CAFs). The immune components, particularly the myeloid cells, play a role in the tumor microenvironment (TME). The myeloid compartment of the TME comprises a functionally diverse compartment of the TME, capable of exerting both pro-tumorigenic and antitumor effects depending on their phenotypic state and microenvironmental cues. In this study, we leveraged single-nucleus multiomic sequencing (snRNA-seq and snATAC-seq) of human pancreatic ductal adenocarcinoma (PDAC) tumors to dissect the immune landscape, particularly the myeloid subsets, in patients who received neoadjuvant chemotherapy, stratified as responders or non-responders based on histopathological evaluation. Our integrative analysis revealed striking differences in myeloid cell composition and transcriptional states, with a notable enrichment of lipid-associated macrophages (LAMs) in chemotherapy responders. LAMs exhibited a transcriptional program enriched for lipid metabolism, phagocytosis, and inflammatory signaling. Pseudotime and chromatin accessibility analyses from the ATAC-Seq data positioned LAMs as terminally differentiated, metabolically reprogrammed macrophages, shaped by immune and environmental cues. ChromVAR analysis identified AP-1, C/EBP, and metabolic transcription factor motif enrichment in LAMs from responders, indicating active immunometabolic states associated with treatment efficacy. These findings implicate LAMs as potential biomarkers and effectors of chemotherapeutic response in PDAC, providing new insights into myeloid cell plasticity and therapeutic reprogramming in the TME.
This dataset
Open GSE300595 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 GSE300595_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 PRJNA1281521 and SRA study SRP594185. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗
Study design
24 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 24 sample titles: 24 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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