From MGUS to multiple myeloma: Unraveling the unknown of precursor states

In the 1960s, through laboratory-based investigations of peripheral blood partnered with detailed clinical annotations, Dr. Waldenström described a condition he called “benign monoclonal gammopathy”. These patients were asymptomatic with a detectable monoclonal protein, and did not meet imaging and laboratory criteria for multiple myeloma. In 1978, through observational retrospective review of medical records, Dr. Kyle observed that not all cases of monoclonal gammopathy were benign. He introduced the term monoclonal gammopathy of undetermined significance (MGUS) to describe a condition that may potentially progress to multiple myeloma (MM), highlighting clinical inability in predicting which patients might progress. In 1980, Drs. Kyle and Greipp described 6 cases which did not fit the definitions of MGUS or MM, and they remained asymptomatic after at least 5 years of follow-up; they were proposed to have smoldering multiple myeloma (SMM). Over time, SMM was defined by arbitrary numerical values (≥10 % plasma cells in the bone marrow and serum M-protein concentration ≥ 3 g/dL). Numerous clinical scores have been developed to define high-risk groups for progression to MM. Current statistical models for progression provide only average risk scores, offering limited clinical utility since the risk of progression at an individual level remains unknown. Physician-scientists are focusing on emerging technologies, such as whole genome sequencing, tumor microenvironment analysis, and single-cell RNA sequencing, to understand precursor states at a molecular level. The overarching goal of these technologies is to better characterize monoclonal gammopathy and other myeloma precursor states. This will enable clinicians to provide more precise, individualized risk assessments and ultimately improve patient outcomes. This review outlines the history of MM precursor states, current definitions, challenges in risk stratification models, and the role of emerging technologies in enhancing predictions and outcomes.