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Expressed mutated genes in Sezary syndrome and their potential prognostic value in patients treated with Extracorporeal Photopheresis

GSE302772 Homo sapiens (human) Single-nucleus RNA-seq 40 samples Submitted 2025/07/23 Platform GPL18573
At a glance

Single-nucleus RNA-seq · human · 40 samples

What it is
Single-nucleus RNA-seqGene activity measured in individual nuclei (used when whole cells are hard to isolate, for example from frozen tissue). 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
40 samples: 19 conditions, mostly in duplicateGroups 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
Expressed mutated genes in Sezary syndrome and their potential prognostic value in patients treated with extracorporeal photopheresisFrontiers in immunology · 2025
Summary (written by the submitters)
Background. Sézary syndrome (SS) is an aggressive and leukemic variant of Cutaneous T-cell Lymphoma (CTCL) with an incidence of 1-case per million people per year. It is characterized by a complex and heterogeneous profile of genetic alterations that has so far precluded the development of a specific and definitive therapeutic intervention.Methods.
Read the full summary ↓
Deep-RNA-sequencing (RNA-seq) data were used to analyze the single nucleotide variants (SNVs) carried by 128 putative CTCL-driver genes, previously identified as mutated in genomic studies, in longitudinal SS samples collected from 17 patients subjected to extracorporeal photopheresis (ECP) with Interferon-α. Results obtained were integrated with Whole Exome Sequencing (WES) data. SNVs were validated using the Sanger method. Pathway analysis was performed with g:Profiler web server (https://biit.cs.ut.ee/gprofiler/gost). Statistical analyses were performed with GraphPad PRISM 8 software.Results.Nonsynonymous SNVs were identified in 56 genes. Integration of RNA-seq with WES data revealed that about half of these genes contained somatic mutations. Among them, the most frequently transcribed mutated genes were TET2, JAK3, NCOR1, PDCD11, RHOA, and TP53.Nearly all the remaining genes had germline-restricted mutations, and included ARID1A, ATM, ATR, CREBBP, POLD1, and POT1 genes, which are involved in DNA repair, homologous recombination, and chromatin remodeling, and the CROCC gene, implicated in centrosome cohesion. Monitoring of the mutated genes, identified within an enlarged panel of CTCL associated genes, revealed their reduction in almost 70% of SS patients as well as a significant decline of total number of mutations (SNVs) during ECP treatment. Several mutated genes persisted post-therapy, representing novel candidates associated with ECP resistance that could also have a potential prognostic relevance.Notably, these genes mainly converge on pathways related to DNA repair (ATR, ATRIP, POLD1, TP53, TP53BP1/2) which might represent novel targets to be explored in combination with ECP.Conclusions. This is the first evaluation in SS of expressed mutations in a large panel of CTCL-driver genes. Also innovative is the monitoring of mutated genes in patients' malignant lymphocytes during ECP, a first-line treatment of In review CTCL, which highlights novel candidates associated with ECP resistance that might unmask novel pharmacological vulnerabilities to be exploited during ECP for a personalized treatment
Published in
Expressed mutated genes in Sezary syndrome and their potential prognostic value in patients treated with extracorporeal photopheresis
Cristofoletti C, Salvatore G, Bassi C et al. · Frontiers in immunology 2025 · PMID 40918137 · doi:10.3389/fimmu.2025.1589467
This dataset
Open GSE302772 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 GSE302772_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 PRJNA1292147 and SRA study SRP601208. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 40918137) ↗
Study design
19 conditions, mostly in duplicate
Purified CD4+ lymphocytes, sample 67T ×3 Purified CD4+ lymphocytes, sample HD ×3 Purified CD4+ lymphocytes, sample 32T ×2 Purified CD4+ lymphocytes, sample 45T ×2 Purified CD4+ lymphocytes, sample 50T ×2 Purified CD4+ lymphocytes, sample 60T ×2 Purified CD4+ lymphocytes, sample 63T ×2 Purified CD4+ lymphocytes, sample 69T ×2 +11 more

Supports a between-group comparison across 40 samples.

How we read this: 19 replicated groups read from 40 sample titles; they account for 40 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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