Multiple Myeloma Insights from Single-Cell Analysis: Clonal Evolution, the Microenvironment, Therapy Evasion, and Clinical Implications

Abstract

Multiple myeloma (MM) is a complex and heterogeneous hematologic malignancy characterized by clonal evolution, genetic instability, and interactions with a supportive tumor microenvironment. These factors contribute to treatment resistance, disease progression, and significant variability in clinical outcomes among patients. This review explores the mechanisms underlying MM progression, including the genetic and epigenetic changes that drive clonal evolution, the role of the bone marrow microenvironment in supporting tumor growth and immune evasion, and the impact of genomic instability. We highlight the critical insights gained from single-cell technologies, such as single-cell transcriptomics, genomics, and multiomics, which have enabled a detailed understanding of MM heterogeneity at the cellular level, facilitating the identification of rare cell populations and mechanisms of drug resistance. Despite the promise of advanced technologies, MM remains an incurable disease and challenges remain in their clinical application, including high costs, data complexity, and the need for standardized bioinformatics and ethical considerations. This review emphasizes the importance of continued research and collaboration to address these challenges, ultimately aiming to enhance personalized treatment strategies and improve patient outcomes in MM.

Keywords: clonal evolution; multiple myeloma; myeloma; omics; personalized medicine; scRNA-seq; single cell; single-cell technologies; tumor microenvironment.

Figure 1

Different types of single−cell sequencing technologies that have been used for multiple myeloma research. Developments in single-omics such as transcriptomics (ex: 10x Chromium and SMART−seq2) and proteomics (ex: antibody-based vs. mass-spectrometry based) have led to integrative analyses in multi-omics, which combine multiple categories of single-cell technologies in methods such as CITE−seq and RAID.

Figure 2

Representation of subclonal diversity and the role of TP53 mutations in multiple myeloma progression and treatment resistance. Created in BioRender. Peyton, M. (2025) https://BioRender.com/d94o089 (accessed on 11 February 2025).

Figure 3

Stem cell differentiation and pathogenesis in multiple myeloma. Normal tissue: Stem cells differentiate into B lymphocytes, which further mature into plasma cells capable of producing normal antibodies, ensuring a functional immune response. Multiple myeloma: Genotoxic stress or other factors lead to damage in B lymphocytes. This damage disrupts normal differentiation, resulting in the formation of malignant myeloma cells that produce abnormal antibodies. These cells propagate within the bone marrow, contributing to the progression of MM. Created in BioRender. Liu, J. (2025) https://BioRender.com/w56y431 (accessed on 27 January 2025).

Figure 4

Liquid biopsy in multiple myeloma for personalized treatment strategies. (A) Different patients with MM represent the variability in disease presentation and progression. (B) Liquid biopsy enables non-invasive sampling and analysis of blood for circulating myeloma cells, genetic and epigenetic alterations, such as amplifications, deletions, chromosomal abnormalities, mutations, and translocations. It includes studies on protein expression and phosphorylation as well as in vitro culture for functional assessments. (C) Insights from liquid biopsy guide the development of personalized treatment strategies tailored to the molecular and cellular characteristics of each patient’s disease, improving therapeutic outcomes. Created in BioRender. Liu, J. (2025) https://BioRender.com/q52m439 (accessed on 27 January 2025).

Figure 5

MM microenvironment and immunotherapeutic strategies. (A). Immune and stromal cells in MM microenvironment: The MM microenvironment includes T cell exhaustion (accompanied by decreased CD4+ T cells), increased regulatory T cells (Tregs), immunosuppressive regulatory B cells (Bregs), M2-polarized macrophages, and dysfunctional dendritic cells. Cancer-associated fibroblasts (CAFs) support tumor growth, myeloma cells stimulate osteoclast activity, and VEGF secreted by myeloma cells (and other microenvironmental cells) drives abnormal blood vessel formation. (B). CAR-T cell therapy: CAR-T cells target MM antigens (e.g., BCMA, CD38, GPRC5D), releasing granzymes and perforins to induce MM cell death. (C). Bispecific antibody therapy: Bispecific antibodies link T cells to MM cells by binding to CD3 on T cells and MM antigens (e.g., BCMA, CD38, FCRH5, GPRC5D), activating T cells to kill MM cells. Created in BioRender. Zhang, Z. (2025) https://BioRender.com/p98h834 (accessed on 27 January 2025).

Figure 6

Single-cell technologies and their clinical application. (A) Single-cell technologies like scDNA-seq, scRNA-seq, and emerging methods such as scATAC-seq and single-cell proteomics contribute to studying the complexity and potential clinical treatment of MGUS, SMM, and MM. (B) The scRNA-seq can be used to identify the dynamics and heterogeneity of both tumor and immune cells by cell-type clustering and expression signature identification. (C) Single-cell technologies enable in-depth studies of cellular interactions within the tumor microenvironment. (D) Using scDNA-seq can also help to detect treatment responses and drug resistance amid clonal evolution. (E) Such early-stage clinical application of single-cell profiling that facilitates its further clinical translation of single-cell technologies includes diagnosis, treatment decision, prognostication, and monitoring of treatment response and residual disease in MM patients. Created in BioRender. Shi, Z. (2025) https://BioRender.com/q34l594 (accessed on 10 February 2025).