Single-cell RNA sequencing reveals cellular heterogeneity and molecular features of extramedullary-involved multiple myeloma Free

Objective: Extramedullary multiple myeloma (EMM) is a highly aggressive subtype of multiple myeloma (MM), characterized by the dissemination of malignant plasma cells beyond the bone marrow (BM) microenvironment. This subtype is frequently associated with treatment resistance and poor prognosis. However, the cellular and molecular landscape of EMM, particularly in paired BM and extramedullary tissue samples, remains poorly understood. This study aims to systematically dissect the cellular heterogeneity, genomic alterations, and immune interactions in EMM using single-cell RNA sequencing (scRNA-seq), thereby identifying key genes, signaling pathways, and transcriptional regulatory mechanisms underlying tumor progression, immune evasion, and subclonal adaptation in extramedullary sites.

Methods: Paired BM and extramedullary tumor samples were collected from five MM patients with confirmed extramedullary metastasis and subjected to scRNA-seq. Cellular subpopulations were identified and compared between BM and extramedullary compartments. Functional enrichment analysis was conducted to characterize biological processes in tumor subsets. Genomic instability was inferred at the single-cell level using the inferCNV (inferring Copy Number Variation) algorithm. SCENIC (Single-cell regulatory network inference and clustering) was applied to reconstruct transcription factor regulatory networks and assess differential regulatory activity. Cell-cell communication patterns between tumor and microenvironmental cells were analyzed using CellPhoneDB to reveal potential mechanisms of immune evasion and stromal interactions in extramedullary lesions.

Results: A transcriptional atlas of extramedullary-involved plasma cells was constructed, revealing a distinct subpopulation characterized by low proliferative capacity, metabolic reprogramming, and immunosuppressive features. inferCNV analysis demonstrated a more complex copy number variation (CNV) landscape in extramedullary plasma cells compared to their BM counterparts, reflecting subclonal adaptation under distinct microenvironmental pressures. Differential gene expression analysis identified core regulatory genes shared across compartments, including TCEB2, ATPIF1, and NCL, associated with transcriptional control, metabolic adaptation, and cell survival. Upregulation of stress-response genes such as HSP90AB1, HSP90AA1, and LGALS1 suggested enhanced cellular resilience in adverse conditions. SCENIC analysis revealed dynamic changes in the activity of key transcription factors in extramedullary plasma cells, including GATA4, IRF9, MAFB, and USF2, indicating transcriptional reprogramming associated with metastatic adaptation. Immune profiling uncovered a highly immunosuppressive extramedullary microenvironment with enrichment of inhibitory immune cells and attenuated tumor-immune cell interactions. CellPhoneDB analysis further identified enhanced crosstalk between tumor cells and stromal components, which may contribute to immune evasion and tumor persistence.

Conclusion: This study provides a comprehensive single-cell transcriptomic characterization of MM with extramedullary involvement, revealing a unique extramedullary tumor cell phenotype marked by low proliferation, metabolic reprogramming, and pronounced genomic instability. Key regulatory genes (TCEB2, ATPIF1, NCL) and stress-adaptive molecules (HSP90AB1, LGALS1) were identified as potential mediators of tumor survival. Transcription factor remodeling (GATA4, IRF9) and extensive tumor-stromal interactions in the immunosuppressive extramedullary niche highlight potential mechanisms of immune escape and metastatic fitness. These findings offer novel insights into the biological underpinnings of extramedullary myeloma and may inform therapeutic strategies targeting extramedullary disease.