GEO series

Loss of IL13RA2 promotes metastatic tumour growth in triple-negative breast cancer via increased AKT signalling

GSE290037 Homo sapiens (human) RNA-seq (gene expression) 21 samples Submitted 2025/02/24 Platform GPL24676Platform GPL24247

At a glance

RNA-seq (gene expression) · human · 21 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: Homo sapiens (human)
Topic: Breast cancerThis series is part of our curated briefing on it.
Samples: 21 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.

Summary (written by the submitters)

Triple-negative breast cancer is characterized by absence of oestrogen receptor, progesterone receptor, and a lack of HER2 amplification and is associated with poor patient prognosis and high rates of distant metastasis. These patients are at elevated risk of brain metastasis, which remains a major therapeutic challenge. IL13RA2, a high-affinity receptor for IL13, is highly expressed in primary brain cancers, many extracranial solid tumours, and in lung- and brain-seeking metastatic variant cell lines.

Read the full summary ↓

However, the relationship between IL13RA2 and patient prognosis is variable, and the biological function of this receptor in cancer remains controversial. We sought to define the role of IL13RA2 in TNBC growth and metastasis, with an emphasis on breast-to-brain metastasis. We identified a positive correlation between elevated IL13RA2 mRNA expression and overall survival in a publicly available dataset of basal-like breast cancer patients. We generated IL13RA2-CRISPR knockout derivatives of the human brain-seeking breast cancer cell line MDA231BrM2 as well as murine 4T1 cells and evaluated changes in gene expression, proliferation, survival, and metastatic growth in vivo. We find that both IL13RA2-deficient models demonstrate enhanced survival and proliferation in vitro, as well as augmented metastatic tumour growth and worsened survival in intracardiac models of brain metastasis. Mechanistically, we observe increased AKT and NF-κB signalling in IL13RA2-deficient cells, which renders these cells highly sensitive to inhibition of AKT. We conclude that, in triple negative breast cancer, loss of IL13RA2 in tumour cells is strongly pro-metastatic, antiapoptotic, and pro-proliferative. These phenotypes are mediated through the AKT and NF-κB pathways. IL13RA2-deficient cells are highly vulnerable to AKT inhibition, which may represent a clinically useful vulnerability for patients with IL13RA2-low tumours. However, our data suggest that inhibition of IL13RA2, though promising in other tumour contexts, may be deleterious in metastatic triple-negative breast cancer.

This dataset

Open GSE290037 in finder → Find more RNA-seq datasets → Breast cancer landscape →

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 GSE290037_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 PRJNA1225945 and SRA study SRP565189. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗

Samples in this study

The sample list for this study is not cached yet. Press Load samples and it will be fetched from NCBI.

+ 21 more — browse all 21 samples with per-sample file links →

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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.