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December 12, 2019

Moving From Cancer Burden to Cancer Genomics for Smoldering Myeloma: A Review

Author Affiliations
  • 1Myeloma Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, New York
  • 2Cancer Genome Project, Wellcome Trust Sanger Institute, Cambridgeshire, United Kingdom
  • 3Department of Oncology and Hemato-Oncology, University of Milan, Milan, Italy
  • 4Department of Hematology, Fondazione IRCCS Istituto Nazionale dei Tumori, Milan, Italy
  • 5Jerome Lipper Multiple Myeloma Center, Dana-Farber Cancer Institute, Harvard Medical School, Boston, Massachusetts
  • 6Veterans Administration Boston Healthcare System, West Roxbury, Massachusetts
JAMA Oncol. 2020;6(3):425-432. doi:10.1001/jamaoncol.2019.4659

Importance  All patients who develop multiple myeloma have a preceding asymptomatic expansion of clonal plasma cells, clinically recognized as monoclonal gammopathy of undetermined significance or smoldering multiple myeloma (SMM). During the past decade, significant progress has been made in the classification and risk stratification of SMM.

Observations  This review summarizes current clinical challenges and discusses available models for risk stratification in the context of SMM. Owing to several novel, more effective, and less toxic drugs, these aspects are becoming increasingly important to identify patients eligible for early treatment. However, all proposed criteria were built around indirect markers of disease burden and therefore are generally able to identify a fraction of patients with SMM in whom transformation to multiple myeloma and genomic subclonal diversification are already happening. In contrast, next-generation sequencing approaches have the potential to identify myeloma precursor disease that will progress even before the major clonal expansion and progression, providing a potential base for more effective treatment and better precision regarding the optimal timing of treatment initiation.

Conclusions and Relevance  Owing to modern technologies, in the near future, prognostic models derived from genomic signatures independent of the disease burden will allow better identification of the optimal timing to treat a plasma cell clonal disorder at the very early stages, when the chances of eradication are higher.

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