← BioTransfer GEO Dataset Finder
GEO series

CARD11 gain of function upregulates BCL2A1 and promotes resistance to targeted therapies combination in B-cell lymphoma

GSE239353 Homo sapiens (human) Single-cell RNA-seq 18 samples Submitted 2025/12/01 Platform GPL24676
At a glance

Single-cell RNA-seq · human · 18 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)
Topic
Mantle cell lymphomaThis series is part of our curated briefing on it.
Samples
18 samples: 18 samples, each with a distinct title — no replicated groupsGroups 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
CARD11 gain of function upregulates BCL2A1 expression and promotes resistance to targeted therapies combination in B-cell lymphomaBlood · 2023
Summary (written by the submitters)
Strategy combining targeted therapies is effective in B-cell lymphomas such as mantle cell lymphoma (MCL), but acquired resistances remain a recurrent issue. Herein, we performed integrative longitudinal genomic and single-cell RNA-seq analyses of MCL patients treated with targeted therapies against CD20, BCL2 and BTK (i.e., OAsIs trial).
Read the full summary ↓
We revealed the emergence of subclones with a selective advantage against OAsIs combination in vivo and showed that resistant cells were characterized by BCR-independent overexpression of NFkB1 target genes, especially due to CARD11 mutations. Functional studies demonstrated that CARD11 gain of function not only resulted in BCR independence, but also directly increase the transcription of the antiapoptotic BCL2A1, leading to venetoclax and OAsIs combination resistance. Based on the transcriptional profile of OAsIs-resistant subclones, we designed a 16-gene resistance signature that was also predictive for MCL patients treated with conventional chemotherapy, underlying a common escape mechanism. Among druggable strategies to inhibit CARD11-dependent NFkB1 transduction, we evaluated the selective inhibition of its essential partner MALT1. We demonstrated that MALT1 protease inhibition led to the reduction of genes involved in OAsIs resistance, including BCL2A1. Consequently, MALT1 inhibition induced synergistic cell death in combination with BCL2 inhibition, irrespective of CARD11 mutational status, both in vitro and in vivo. Taken together our study identified mechanisms of resistance to targeted therapies and provided a novel strategy to overcome resistance in aggressive B-cell lymphoma.
Published in
CARD11 gain of function upregulates BCL2A1 expression and promotes resistance to targeted therapies combination in B-cell lymphoma
Decombis S, Bellanger C, Le Bris Y et al. · Blood 2023 · PMID 37562004 · doi:10.1182/blood.2023020211
This dataset
Open GSE239353 in finder → Find more human RNA-seq datasets → Mantle cell lymphoma 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 GSE239353_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 PRJNA998886 and SRA study SRP451603. Searching any of these in the dataset finder brings you back here.

NCBI GEO page ↗ Paper (PMID 37562004) ↗
Study design
18 samples, each with a distinct title — no replicated groups

Any grouping that defines a comparison lives in the sample characteristics, which GEO records separately from the titles.

How we read this: Read from 18 sample titles: 18 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
More Mantle cell lymphoma datasets

Search all human RNA-seq datasets in GEO →

Share this dataset

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.