GEO series

Beyond CD30 Targeting: Brentuximab Vedotin Triggers Immunogenic Cell Death and Antitumor Immunity in Mycosis Fungoides, Overcoming Resistance via BCL2 Inhibition

GSE303446 Homo sapiens (human) Single-cell RNA-seq 13 samples Submitted 2026/05/20 Platform GPL24676

At a glance

Single-cell RNA-seq · human · 13 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: 13 samples: 12 distinct titles, 1 of them repeatedGroups 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: Beyond CD30: Dual-Targeting of Malignant and Regulatory T Cells by Brentuximab Vedotin Remodels the Lymphoma Microenvironment and Overcomes Resistance via BCL2 Inhibition in Mycosis FungoidesAdvanced science (Weinheim, Baden-Wurttemberg, Germany) · 2026

Summary (written by the submitters)

Background: Mycosis fungoides (MF), the most common subtype of cutaneous T cell lymphoma (CTCL), has poor prognosis in advanced stages due to lack of effective therapies. Brentuximab vedotin (BV), an antigen-drug conjugate targeting CD30, is approved for CD30+ MF after prior systemic treatment. However, many patients develop resistance with unclarified mechanisms.

Read the full summary ↓

Method: We conducted single-cell RNA sequencing on 13 paired tumor samples from 6 CD30+ MF patients treated with BV. Eight post-treatment samples were grouped into responsive (n = 5) and non-responsive (n = 3) lesions based on pathologic response. Combination effects of BV and BCL2 inhibitors were assessed in CD30+ CTCL cell lines. Result: BV induced immunogenic cell death (ICD) in both CD30+ and CD30- malignant T cells, enhancing their MHC-II-mediated antigen presentation in responsive lesions. Besides, BV specifically enhanced the IFNα/γ responses in CD30- malignant T cells. These coincided with TME reprogramming: diminished immunosuppressive function of Tregs and activated antitumor immunity mediated by dendritic cells, CD8+ T and NK cells. In non-responsive lesions, resistance was driven by distinct mechanisms: CD30+ malignant T cells upregulated the drug efflux transporter ABCB1 and exhibited impaired endosomal processing, while CD30- tumor cells overexpressed the anti-apoptotic protein BCL2 and displayed blunted IFN response. We confirmed potent synergy between BV and BCL2 inhibitors in CTCL cell lines, overcoming resistance. Conclusion: Beyond direct CD30+ targeting, BV transforms the immunosuppressive milieu by inducing ICD in tumor cells and suppressing Tregs. Resistance involves ABCB1 upregulation in CD30+ cells and BCL2-mediated survival in CD30- cells. Combining BV with BCL2 inhibitors represents a promising strategy to overcome resistance in CD30+ CTCL.

Published in

Beyond CD30: Dual-Targeting of Malignant and Regulatory T Cells by Brentuximab Vedotin Remodels the Lymphoma Microenvironment and Overcomes Resistance via BCL2 Inhibition in Mycosis Fungoides

Jiang Y, Lai P, Li M et al. · Advanced science (Weinheim, Baden-Wurttemberg, Germany) 2026 · PMID 41762706 · doi:10.1002/advs.202517353

This dataset

Open GSE303446 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 GSE303446_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 PRJNA1295448 and SRA study SRP603043. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 41762706) ↗

Study design

12 distinct titles, 1 of them repeated

Skin, response, P3 ×2

How we read this: Read from 13 sample titles: 12 distinct titles with little repetition. 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

GSM9126888 Skin, pre-treatment, P1_pre
GSM9126889 Skin, pre-treatment, P2_pre
GSM9126890 Skin, response, P2_R
GSM9126891 Skin, non-response, P2_NR
GSM9126892 Skin, pre-treatment, P3_pre
GSM9126893 Skin, response, P3_R1
GSM9126894 Skin, response, P3_R2
GSM9126895 Skin, pre-treatment, P4_pre
GSM9126896 Skin, response, P4_R
GSM9126897 Skin, non-response, P5_NR
GSM9126898 Skin, pre-treatment, P6_pre
GSM9126899 Skin, response, P6_R
GSM9126900 Skin, non-response, P6_NR

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