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IL4Ra blockade inhibits proliferation of malignant lymphocytes and the immunosuppressive tumor microenvironment of mycosis fungoides

GSE293752 Homo sapiens (human) Single-cell RNA-seq 23 samples Submitted 2026/07/08 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 23 samples

What it is
Single-cell RNA-seqGene activity measured in thousands of individual cells, so cell types can be told apart. Read from the summary: GEO's own type field only says 'expression profiling by sequencing', so confirm it in the paper.
Organism
Homo sapiens (human)
Samples
23 samples: 13 × MF vs 4 × NSGroups 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
IL4Rα blockade inhibits proliferation of malignant lymphocytes and the immunosuppressive tumor microenvironment of mycosis fungoidesJournal for immunotherapy of cancer · 2026
Summary (written by the submitters)
The IL4 and IL13 receptors are highly expressed in the mycosis fungoides (MF) cutaneous tumor microenvironment (TME). By combining single-cell RNA sequencing with inhibition of IL4Ra with Dupilumab (REGN668), a monoclonal antibody that targets the common subunit of the IL4 and IL13 receptors, we have identified the genes and pathways regulated by IL4/IL13 signaling in the MF TME.
Read the full summary ↓
We found that Dupilumab inhibits patient-specific processes in malignant lymphocytes, and we also identified downregulation of common pathways among patient samples including those associated with cell cycle progression, PI3K/AKT signaling, JAK/STAT signaling, DNA damage/repair, and TP53 regulation. Using ex vivo skin explants assays from advanced MF skin tumors we demonstrated that Dupilumab inhibits proliferation of MF malignant lymphocytes without affecting their survival. Furthermore, we showed that IL4Ra blockade in the TME of advanced MF downregulates multiple immunosuppressive pathways associated with myeloid-derived suppressive cell and LAMP3+ dendritic cell recruitment and function, cytokine production and antigen presentation in B cells as well as differentiation, and degranulation of mast cells. These results provide new insights into advanced MF tumorigenesis including the nature of Dupilumab interactions within the MF TME. However, the extensive genetic, transcriptional, and functional heterogeneity across MF patients indicates that inhibition of multiple driver pathways and a personalized therapeutic approach, which may include Dupilumab, will be essential for developing new therapeutic strategies against this incurable disease.
Published in
IL4Rα blockade inhibits proliferation of malignant lymphocytes and the immunosuppressive tumor microenvironment of mycosis fungoides
Gaydosik AM, Wang A, Das J et al. · Journal for immunotherapy of cancer 2026 · PMID 42350047 · doi:10.1136/jitc-2026-015022
This dataset
Open GSE293752 in finder → Find more human 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 GSE293752_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 PRJNA1246151. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 42350047) ↗
Study design
13 × MF vs 4 × NS

Supports a between-group comparison across 17 samples.

How we read this: 2 replicated groups read from 23 sample titles; they account for 17 of them. 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
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