GEO series

TLR4-dependent fibroblast-monocyte axis in tumor-draining lymph nodes contributes to metastasis in triple-negative breast cancer

GSE267535 Mus musculus (mouse) RNA-seq (gene expression) 10 samples Submitted 2025/09/17 Platform GPL30172

At a glance

RNA-seq (gene expression) · mouse · 10 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: Mus musculus (mouse)
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: A TLR4-dependent fibroblast-monocyte axis in tumor-draining lymph nodes contributes to metastasis in triple-negative breast cancerImmunity · 2025

Summary (written by the submitters)

Triple-negative breast cancer (TNBC) is the most aggressive form of BC and despite improvements in neoadjuvant treatment with immune checkpoint blockade (ICB) targeting PD-1, response rates vary, and patients relapse with metastatic disease. Using human samples and mouse models, we show that myeloid cells, particularly monocytes, increase in aggressive TNBC draining lymph nodes (TDLN) showing T-cell suppressive capacity via PD-L1 and iNOS.

Read the full summary ↓

Tumor-induced TLR4 ligands reprogrammed TDLN fibroblastic reticular cells (FRCs) through TLR4 activation to induce the recruitment of monocytes via CCL2 and CCL7 in vascularized and T-cell areas. Indeed, monocytes expressing the receptor of CCL2/7 CCR2 home to and colocalize with FRC-rich niches. The local inhibition of this recruitment axis at the TDLN site, either through CCR2 or TLR4 inhibition, in combination with PD-1 blocking, resulted in reduced LN and distant lung metastasis in mouse models. A subset of TNBC patients with worse survival was stratified using TLR4 ligands, including TNC, S100A9, and FN1. Our results thus demonstrate a novel mechanism through which metastatic TNBC tumors reprogram the TDLN-niche to facilitate PD-L1-mediated immune evasion that precedes and drives metastasis. We thereby provide an opportunity to improve the efficacy of ICB in TNBC patients and to use the TDLN or our primary tumor signature as predictive biomarkers.

Published in

A TLR4-dependent fibroblast-monocyte axis in tumor-draining lymph nodes contributes to metastasis in triple-negative breast cancer

Mattavelli G, Helal M, Cetkovic A et al. · Immunity 2025 · PMID 40957419 · doi:10.1016/j.immuni.2025.08.015

This dataset

Open GSE267535 in finder → Find more mouse 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 GSE267535_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 PRJNA1111791 and SRA study SRP507823. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40957419) ↗

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

GSM8268374 TDLN-NDLN_axLN_FRCs_Stroma_m_002_GX
GSM8268375 TDLN-NDLN_axLN_FRCs_Stroma_m_002_HTO
GSM8268376 TDLN-NDLN_axLN_Myeloid_Stroma_m_001_GX
GSM8268377 TDLN-NDLN_axLN_Myeloid_Stroma_m_001_HTO
GSM8268378 TDLN-NDLN_axLN_T-cells_Stroma_m_002_GX
GSM8268379 TDLN-NDLN_axLN_T-cells_Stroma_m_002_HTO
GSM8268380 Atlas_axLN_T-B-myeloid_GX
GSM8268381 Atlas_axLN_T-B-myeloid_CITEseq
GSM8268382 Atlas_medLN_T-B-myeloid_GX
GSM8268383 Atlas_medLN_T-B-myeloid_CITEseq

Datasets on a similar topic

GSE267533 TLR4-dependent fibroblast-monocyte axis in tumor-draining lymph nodes contributes to metastasis in triple-negative breast cancer 6 samples shares fibroblast-monocyte, triple-negative, tlr4-dependent, tumor-draining, contributes, metastasis, breast, lymph
GSE207977 Triple therapy reduced metastasis in triple-negative breast cancer 2 samples shares triple-negative, metastasis, breast
GSE304115 Bulk RNA-seq of Genetically Diverse Triple-Negative Breast Cancer Mouse Models Treated with Immunotherapy, Chemotherapy, Erlotinib, or Trametinib 309 samples shares triple-negative, breast
GSE273940 Low Molecular Weight Cyclin E Confers a Vulnerability to PKMYT1 Inhibition in Triple-Negative Breast Cancer 33 samples shares triple-negative, breast
GSE311488 Chemotherapeutic Induction of Single-Stranded DNA Accumulation Sensitizes Triple-Negative Breast Cancer to Immunotherapy [scRNA-seq] 32 samples shares triple-negative, breast
GSE294999 Obesity-driven inflammation enables triple-negative breast cancer progression through immunosuppression [scRNA-Seq] 16 samples shares triple-negative, breast
GSE276098 Immunosuppressive microenvironment restrains chemotherapy efficacy in liver metastases of triple-negative breast cancer 16 samples shares triple-negative, breast
GSE317281 Integrated bulk and single-cell transcriptome analysis reveals the impact of cyclophosphamide on tumor microenvironment in triple-negative breast cancer models 14 samples shares triple-negative, breast

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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.