The Role of Clonal Evolution on Progression, Blood Parameters, and Response to Therapy in Multiple Myeloma

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

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.

Chromosomal defects in multiple myeloma

Multiple myeloma is a plasma cell neoplasm driven by primary (e.g. hyperdiploidy; IGH translocations) and secondary (e.g. 1q21 gains/amplifications; del(17p); MYC translocations) chromosomal events. These are important to detect as they influence prognosis, therapeutic response and disease survival. Currently, cytogenetic testing is most commonly performed by interphase fluorescence in situ hybridisation (FISH) on aspirated bone marrow samples. A number of variations to FISH methodology are available, including prior plasma cell enrichment and incorporation of immunophenotypic plasma cell identification. Other molecular methods are increasingly being utilised to provide a genome-wide view at high resolution (e.g. single nucleotide polymorphism (SNP) microarray analysis) and these can detect abnormalities in most cases. Despite their wide application at diagnostic assessment, both FISH and SNP-array have relatively low sensitivity, limiting their use for identification of prognostically significant low-level sub-clones or for disease monitoring. Next-generation sequencing is increasingly being used to detect mutations and new FISH techniques such as by flow cytometry are in development and may address some of the current test limitations. Here we review the primary and secondary cytogenetic aberrations in myeloma and discuss the range of techniques available for their assessment.

clevRvis: visualization techniques for clonal evolution

BACKGROUND

A thorough analysis of clonal evolution commonly requires integration of diverse sources of data (e.g., karyotyping, next-generation sequencing, and clinical information). Subsequent to actual reconstruction of clonal evolution, detailed analysis and interpretation of the results are essential. Often, however, only few tumor samples per patient are available. Thus, information on clonal development and therapy effect may be incomplete. Furthermore, analysis of biallelic events-considered of high relevance with respect to disease course-can commonly only be realized by time-consuming analysis of the raw results and even raw sequencing data.

RESULTS

We developed clevRvis, an R/Bioconductor package providing an extensive set of visualization techniques for clonal evolution. In addition to common approaches for visualization, clevRvis offers a unique option for allele-aware representation: plaice plots. Biallelic events may be visualized and inspected at a glance. Analyzing 4 public datasets, we show that plaice plots help to gain new insights into tumor development and investigate hypotheses on disease progression and therapy resistance. In addition to a graphical user interface, automatic phylogeny-aware color coding of the plots, and an approach to explore alternative trees, clevRvis provides 2 algorithms for fully automatic time point interpolation and therapy effect estimation. Analyzing 2 public datasets, we show that both approaches allow for valid approximation of a tumor's development in between measured time points.

CONCLUSIONS

clevRvis represents a novel option for user-friendly analysis of clonal evolution, contributing to gaining new insights into tumor development.