Genomic Risk Stratification Could Transform Smoldering Myeloma Management
Current smoldering multiple myeloma risk models rely on indirect markers of disease burden that only identify patients in whom progression is already underway. This JAMA Oncology review argues that NGS-based genomic approaches can detect subclonal diversification and predict progression earlier, before major clonal expansion occurs. With more effective and less toxic therapies available, genomic signatures independent of tumour burden may enable optimal timing of early treatment when chances of eradication are highest.
The original study
Moving From Cancer Burden to Cancer Genomics for Smoldering Myeloma: A Review.
- Authors
- Maura F, Bolli N, Rustad EH, Hultcrantz M, Munshi N, Landgren O
- Journal
- JAMA oncology
- Type
- Journal Article, Review
- PMID
- 31830214
Original abstract
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.