GEO series

Targeting IRE1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate cancer [scRNA-seq]

GSE240382 Mus musculus (mouse) Single-cell RNA-seq 2 samples Submitted 2024/11/22 Platform GPL24247

At a glance

Single-cell RNA-seq · mouse · 2 samples

What it is: Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart.
Organism: Mus musculus (mouse)
Samples: 2 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: Targeting IRE1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate cancerNature communications · 2024

Summary (written by the submitters)

Unfolded protein response (UPR) is a central intracellular stress response pathway that is hijacked by tumor cells for their survival. However, how activation of UPR in cancer cells may shape the tumor microenvironment (TME) remains largely unexplored. Here, we investigated the potential role of IRE1α signaling on modulation of TME dynamics in prostate cancer (PCa).

Read the full summary ↓

We found that IRE1α is increased in PCa patient tumors and genetic inhibition of IRE1α in syngeneic mouse PCa models, as well as in an orthotopic model, dramatically reduced tumor growth. Multiomics analysis suggested that IRE1α ablation in cancer cells significantly potentiated interferon (IFN) response and activation of immune system related pathways in the TME. Single-cell RNA-sequencing (scRNA-seq) revealed that the abundance of immunosuppressive cells, such as tumor-associated macrophages (TAMs), were markedly reduced in the IRE1α deficient tumors. Geneset Enrichment Analysis demonstrated that IFN response was significantly enriched in TAMs, cancer cells, and dendritic cells. Notably, the small molecule IRE1α inhibitor MKC8866 (ORIN1001), currently in clinical trials, reprogrammed the TME and enhanced the response to anti-PD-1 blockade therapy in syngeneic PCa mouse models. Furthermore, a novel scRNA-seq-derived TAM gene signature is strongly associated with poor PCa survival, which is reduced by the MKC8866 + anti-PD-1 combination therapy. Our findings indicate that activation of IRE1α signaling not only promotes cancer cell growth and survival, but it also interferes with anti-tumor immunity in the TME. Thus, IRE1α targeting could present a novel approach for improving anti-PD-1 immunotherapy in PCa.

Published in

Targeting IRE1α reprograms the tumor microenvironment and enhances anti-tumor immunity in prostate cancer

Unal B, Kuzu OF, Jin Y et al. · Nature communications 2024 · PMID 39406723 · doi:10.1038/s41467-024-53039-1

This dataset

Open GSE240382 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 GSE240382_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: Single-cell studies usually ship their counts as 10x files (matrix.mtx, barcodes.tsv, features.tsv), an .h5 file, or an .rds / .h5ad object that Seurat or Scanpy can open. Look for those among the supplementary files.

Also filed as BioProject PRJNA1003475. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 39406723) ↗

Samples in this study

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+ 2 more — browse all 2 samples with per-sample file links →

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