← BioTransfer GEO Dataset Finder
GEO series

A multiomic analysis of Waldenström macroglobulinemia defines distinct disease subtypes

GSE296167 Homo sapiens Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing 26 samples 2025/05/02 GPL24676
Summary
We carried out a single-cell (sc) multiomic analysis on a series of MYD88 mutated Waldenström macroglobulinemia (WM) cases and identified two distinct subtypes of disease, memory B-cell-like (MBC-like) and plasma cell-like (PC-like), based on their expression of key lineage defining genes. Biologically, the subtypes are characterized by their variable capacity to differentiate fully towards a plasma cell (PC) and exhibit unique transcriptomic, chromatin accessibility, and genomic profiles. The MBC-like subtype is unable to differentiate beyond the memory B-cell (MBC) stage, upregulates key MBC genes, and is characterized by upregulated BCR and AKT/mTOR signaling. In contrast, the PC-like subtype can partially differentiate towards a PC, upregulates key PC genes, has enhanced NF-kB signaling, and has an upregulated unfolded protein response. Pseudotime trajectory analysis of combined scRNA-sequencing and scATAC-sequencing supports the variable differentiation capacity of each subtype and implicate key transcription factors SPI1, SPIB, BCL11A, and XBP1 in these features. The existence and generalizability of the two disease subtypes were validated further using hierarchical clustering of bulk RNA-seq data from a secondary set of cases. The biological significance of the subtypes was further established using whole genome sequencing, where it was shown that CXCR4, NIK, and ARID1A mutations occur predominantly in the MBC-like subtype and 6q deletions in the PC-like subtype. We conclude that the variable differentiation blockade seen in WM manifests itself clinically as two disease subtypes with distinct epigenetic, mutational, transcriptional, and clinical features with potential implications for WM treatment strategies.
Download
NCBI GEO page ↗ Paper (PMID 40332467) ↗ {# Names what the click gives you. "Open in finder" meant nothing to a visitor who arrived from a search engine and has never seen the tool. #} Find more human ChIP / ATAC / CUT&Tag datasets →
Similar datasets

Search all human ChIP / ATAC / CUT&Tag 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.