GEO series

Myeloid progenitor dysregulation fuels immunosuppressive macrophages in tumors [dataset 2]

GSE270127 Homo sapiens (human) ChIP-seq / ATAC-seq (genome binding and open chromatin) 10 samples Submitted 2025/07/07 Platform GPL24676

At a glance

ChIP-seq / ATAC-seq (genome binding and open chromatin) · human · 10 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)
Samples: 10 samples: 10 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: Myeloid progenitor dysregulation fuels immunosuppressive macrophages in tumoursNature · 2025

Summary (written by the submitters)

Monocyte–derived macrophages (mo-macs) drive immunosuppression in the tumor microenvironment (TME) and tumor-enhanced myelopoiesis in the bone marrow (BM) fuels these populations. Here, we performed paired transcriptome and chromatin analysis over the continuum of BM myeloid progenitors, circulating monocytes, and tumor-infiltrating mo-macs in mice and in patients with lung cancer to identify myeloid progenitor programs that fuel pro-tumorigenic mo-macs.

Read the full summary ↓

Analyzing chromatin accessibility and histone mark changes, we show that lung tumors prime accessibility for Nfe2l2 (NRF2) in BM myeloid progenitors as a cytoprotective response to oxidative stress. NRF2 activity is sustained and increased during monocyte differentiation into mo-macs in the lung TME to regulate oxidative stress, in turn promoting metabolic adaptation, resistance to cell death, and contributing to immunosuppressive phenotype. NRF2 genetic deletion and pharmacological inhibition significantly reduced mo-macs’ survival and immunosuppression in the TME, enabling NK and T cell therapeutic antitumor immunity and synergizing with checkpoint blockade strategies. Altogether, our study identifies a targetable epigenetic node of myeloid progenitor dysregulation that sustains immunoregulatory mo-macs in the TME.

Published in

Myeloid progenitor dysregulation fuels immunosuppressive macrophages in tumours

Hegde S, Giotti B, Soong BY et al. · Nature 2025 · PMID 40931076 · doi:10.1038/s41586-025-09493-y

This dataset

Open GSE270127 in finder → Find more human RNA-seq datasets →

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 GSE270127_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 PRJNA1125112 and SRA study SRP514493. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40931076) ↗

Study design

10 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 10 sample titles: 10 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

GSM8334928 PBMCs from NSCLC Patient Lu1024 RNA (Multiome [RNA + ATAC])
GSM8334929 PBMCs from NSCLC Patient Lu1027 RNA (Multiome [RNA + ATAC])
GSM8334930 PBMCs from NSCLC Patient Lu1032 RNA (Multiome [RNA + ATAC])
GSM8334931 PBMCs from NSCLC Patient HD4 RNA (Multiome [RNA + ATAC])
GSM8334932 PBMCs from NSCLC Patient HD5 RNA (Multiome [RNA + ATAC])
GSM8334933 PBMCs from NSCLC Patient Lu1024 ATAC (Multiome [RNA + ATAC])
GSM8334934 PBMCs from NSCLC Patient Lu1027 ATAC (Multiome [RNA + ATAC])
GSM8334935 PBMCs from NSCLC Patient Lu1032 ATAC (Multiome [RNA + ATAC])
GSM8334936 PBMCs from NSCLC Patient HD4 ATAC (Multiome [RNA + ATAC])
GSM8334937 PBMCs from NSCLC Patient HD5 ATAC (Multiome [RNA + ATAC])

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