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7aaRGD - a novel SPP1/integrin signaling-blocking peptide reverses immunosuppression and improves anti-PD-1 immunotherapy outcomes in experimental gliomas

GSE266110 Mus musculus (mouse) RNA-seq (gene expression) 40 samples Submitted 2025/04/30 Platform GPL18480
At a glance

RNA-seq (gene expression) · mouse · 40 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
40 samples
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
7aaRGD - a novel SPP1/integrin signaling-blocking peptide reverses immunosuppression and improves anti-PD-1 immunotherapy outcomes in experimental gliomasJournal of experimental & clinical cancer research : CR · 2025
Summary (written by the submitters)
Clinical trials with immune checkpoint inhibitors have benefited many cancer patients but were unsuccessful in a number of tumors, including glioblastoma (GBM), the most common and aggressive primary brain tumor in adults. The main obstacle is the immunosuppressive and continuously evolving tumor microenvironment (TME) in which antitumor immunity is inhibited or eluded by tumor-secreted factors.
Read the full summary ↓
Reprogramming of glioma associated myeloid cells (GAMs) creates a “cold” immunosuppressive TME resulting in a poor infiltration, exhaustion or death of effector T cells. We have developed a synthetic RGD peptide that blocks the reprogramming of GAMs by targeting tumor-GAMs interactions. Here, we explored if the RGD can revert tumor induced changes in the TME of experimental gliomas and improve anti-PD-1 immunotherapy. We demonstrate that RGD efficiently blocks microglia-dependent invasion of murine and human glioma cells in vitro. While RGD alone did not reduce tumor growth in vivo, it prevented tumor-induced transcriptomic changes in GAMs, augmenting the expression of immune response genes. Interestingly, intratumoral RGD delivery led to the normalization of peritumoral blood vessels. Combining RGD with anti-PD-1 antibody reduced tumor growth and induced profound changes in the immune composition of TME as demonstrated using multiparametric flow cytometry. Interferon-ɣ producing capacity of effector T cells and its intratumoral levels increased in mice treated with RGD and anti-PD-1 antibody. Transcriptomic profiles of GAMs were altered by the combined treatment, consistently with the restored inflammatory TME and boosted immunotherapy responses. Intratumoral RGD similarly modified the myeloid cells in TME of human U87MG gliomas in immunocompromised mice.
Published in
7aaRGD - a novel SPP1/integrin signaling-blocking peptide reverses immunosuppression and improves anti-PD-1 immunotherapy outcomes in experimental gliomas
Ellert-Miklaszewska A, Pilanc P, Poleszak K et al. · Journal of experimental & clinical cancer research : CR 2025 · PMID 40281508 · doi:10.1186/s13046-025-03393-9
This dataset
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.

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 GSE266110_RAW.tar (it can run to tens of gigabytes).

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.

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 PRJNA1105903 and SRA study SRP509736. Searching any of these in the dataset finder brings you back here.

Samples in this study

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