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Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistance

GSE293819 Mus musculus (mouse) RNA-seq (gene expression) 142 samples Submitted 2025/12/17 Platform GPL24247
At a glance

RNA-seq (gene expression) · mouse · 142 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
142 samples: 142 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
Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistanceJournal for immunotherapy of cancer · 2025
Summary (written by the submitters)
Immune checkpoint inhibitors (ICI) have improved survival in various cancers, but their success in breast cancer, specifically triple-negative breast cancer (TNBC), remains limited, benefiting less than 10% of patients. We modeled ICI response in vivo to unravel the mechanisms underlying immunotherapy efficacy, identify mechanisms of resistance in non-responsive tumors, and ascertain the therapeutic benefits of different chemotherapeutic combinations with ICI in breast cancer.
Read the full summary ↓
We investigated the impact of ICI as monotherapy and in combination with other therapeutics in mouse models of mammary cancer, which we found robustly suppressed primary tumor growth and extended survival. Interestingly, even within a single model, responses to ICI were highly variable. Resistance was not reliably retained by transplantation into syngeneic hosts, suggesting a role for systemic host immunity rather than tumor-autonomous mechanisms. Transcriptomic analysis of the primary tumor landscape by fine-needle aspiration revealed that upregulated cytotoxic T cell response and inflammatory interferon signaling (both at baseline and post anti-PD-L1 administration) corresponded to favorable response to ICI. Longitudinal analysis of the peripheral blood uncovered enhanced myeloid cell recruitment in resistant mice, prior to therapy initiation. Similar effects were observed through longitudinal assessment of peripheral blood in ICI-treated human breast cancer patients. Blocking myeloid cell recruitment with navaraxin (CXCR1/2 inhibitor) improved ICI responses, further suppressing tumor growth and improving survival. These findings provide insight into resistance mechanisms and suggest the potential for minimally invasive strategies (sampling of systemic immune cells from peripheral blood) to identify patients likely to respond to ICI. This approach may help inform de-escalation strategies to mitigate therapeutic toxicities and limit unnecessary treatments.
Published in
Monitoring systemic immune responses to checkpoint inhibition in breast cancer reveals host responses and mechanisms of resistance
Hanna A, Sun X, Sheng Q et al. · Journal for immunotherapy of cancer 2025 · PMID 41371907 · doi:10.1136/jitc-2025-012606
This dataset
Open GSE293819 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 GSE293819_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 PRJNA1246516 and SRA study SRP576511. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41371907) ↗
Study design
142 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 142 sample titles: 142 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

+ 102 more — browse all 142 samples with per-sample file links →

Datasets on a similar topic

Search all mouse RNA-seq datasets in GEO →

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