Genomically Smoldering Multiple Myeloma Is Not a Distinct Entity But a Collection of Monoclonal Gammopathy of Undetermined Significance or Multiple Myeloma

A Anil Aktas Samur (Dana Farber Cancer Institution, Boston, Massachusetts, United States) J Jill Corre (Unité Génomique du Myélome, Hôpital Universitaire de Toulouse Oncopole, Université de Toulouse, Toulouse, France) S Srikanth Talluri (DFCI, Boston, Massachusetts, United States) P Parth Shah A Antoine Graffeuil (University Cancer Center of Toulouse Institut National de la Santé, Toulouse, France) J Joshua Rivera (1Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA) Y Yiyang Fan B Bahar Dakiki Korucu (Department of Data Science, Dana Farber Cancer Institute, Boston, MA) R Raphael Szalat (2Boston Medical Center, Boston, United States) M Mariateresa Fulciniti (Dana Farber Cancer Institute, Boston, Massachusetts, United States) K Kenneth C. Anderson A Adam Sperling (1Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, United States) G Giovanni Parmigiani H Hervé Avet-Loiseau (Unité Génomique du Myélome, Hôpital Universitaire de Toulouse Oncopole, Université de Toulouse, Toulouse, France) N Nikhil C. Munshi M Mehmet Kemal Samur (Dana-Farber Cancer Institute, Boston, MA)

Abstract

PURPOSE The diagnosis of smoldering multiple myeloma (SMM) primarily relies on clinical features such as plasma cell involvement, immunoglobulin protein levels, and end-organ damage. However, as early intervention becomes a priority, the role of genomic features in differentiating risk is gaining attention. METHODS This study analyzed next-generation sequencing data from 224 precursor condition samples with 51 patients having paired SMM and multiple myeloma (MM) and 1,779 samples from newly diagnosed MM to identify genomic features linked to progression in SMM and those with a low-risk nonprogressor precursor condition. RESULTS Our findings from paired samples revealed no significant differences in somatic alterations and clonal structures between SMM and MM samples from the same patient. This indicates that plasma cells in progressor SMM are genomically pre-equipped with changes that define myeloma. Over 80% of driver mutations were present at both time points, and more than 66% of progressor samples showed only minor clonal changes. We further compared genomic changes between nonprogressor and progressor SMM. Nonprogressor plasma cells showed significantly lower mutational load and the absence of copy number alterations on chromosome 8. They reduced focal genomic loss compared with progressor plasma cells. A scoring system using genomic features predictively identified patients with low-risk SMM unlikely to progress, validated on 101 additional independent samples, and additive clinical value of genomic classification was further shown in combination with 20/2/20. CONCLUSION In summary, the genomic distinctions now suggest that a proportion of SMMs with progressor phenotype are akin to MM, whereas nonprogressor SMM has monoclonal gammopathy of undetermined significance–like characteristics. The results should influence further investigation in larger studies to inform future diagnostic criteria and trial designs.

Article Details

Volume / Issue Vol. 44, Issue 4
Published February 01, 2026
Pages 321-334
ISSN 0732-183X
Publisher Lippincott Williams & Wilkins

Journal Info

Journal of Clinical Oncology

Lippincott Williams & Wilkins

ISSN: 0732-183X Health Sciences

Authors (16)

A

Anil Aktas Samur

Dana Farber Cancer Institution, Boston, Massachusetts, United States

J

Jill Corre

Unité Génomique du Myélome, Hôpital Universitaire de Toulouse Oncopole, Université de Toulouse, Toulouse, France

S

Srikanth Talluri

DFCI, Boston, Massachusetts, United States

P

Parth Shah

A

Antoine Graffeuil

University Cancer Center of Toulouse Institut National de la Santé, Toulouse, France

J

Joshua Rivera

1Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA

Y

Yiyang Fan

B

Bahar Dakiki Korucu

Department of Data Science, Dana Farber Cancer Institute, Boston, MA

R

Raphael Szalat

2Boston Medical Center, Boston, United States

M

Mariateresa Fulciniti

Dana Farber Cancer Institute, Boston, Massachusetts, United States

K

Kenneth C. Anderson

A

Adam Sperling

1Dana-Farber Cancer Institute, Department of Medical Oncology, Boston, United States

G

Giovanni Parmigiani

H

Hervé Avet-Loiseau

Unité Génomique du Myélome, Hôpital Universitaire de Toulouse Oncopole, Université de Toulouse, Toulouse, France

N

Nikhil C. Munshi

M

Mehmet Kemal Samur

Dana-Farber Cancer Institute, Boston, MA