Evolution of multiple myeloma from a genomic perspective

F Francesco Maura (Memorial Sloan Kettering Cancer Center, New York) M Mehmet Samur (2Department of Data Science, Dana-Farber Cancer Institute, Boston) N Nikhil Munshi (3VA Boston Healthcare System, Boston, MA)

Abstract

Abstract In this review, we explore the role of complex interactions between genomic evolution, environmental and genetic predispositions, and immune surveillance in disease progression from precursor conditions smoldering multiple myeloma and monoclonal gammopathy of undetermined significance to multiple myeloma (MM). MM has been described to be universally preceded by precursor states, often decades before it is even diagnosed. Genetic predisposition plays an important role in the initial transformation, and is driven by both germline variants and MM-specific loci influencing risk. The reported disparities in occurrence of precursor conditions and MM among racial groups highlights the role of predisposition and the need for broader cohort studies. Early genomic events, such as translocations and hyperdiploidy, are essential in precursor initiation. However, additional factors are usually needed to transform the precursor stages into symptomatic disease, such as positive selection of subclonal populations. This process is affected by aging and environmental factors, such as exposures to Agent Orange and agrochemicals. Therefore, integrating genomic and transcriptomic data with immune profiling or other clinical features is essential for identifying patients with high risk of progressing into MM. Here, we highlight the complexity of myelomagenesis, and underline the importance of state-of-the-art approaches for improved disease prediction.

Article Details

Journal Blood
Volume / Issue Vol. 147, Issue 13
Published March 26, 2026
Pages 1423-1432
ISSN 0006-4971
Publisher Elsevier BV

Journal Info

Blood

Elsevier BV

ISSN: 0006-4971 Health Sciences

Authors (3)

F

Francesco Maura

Memorial Sloan Kettering Cancer Center, New York

M

Mehmet Samur

2Department of Data Science, Dana-Farber Cancer Institute, Boston

N

Nikhil Munshi

3VA Boston Healthcare System, Boston, MA