Abstract Multiple myeloma (MM) arises from abnormal plasma cells (PCs) progressing from precursor states, including monoclonal gammopathy of undetermined significance (MGUS) and smoldering multiple myeloma (SMM). Understanding this transition and progression to overt MM requires improved non-invasive strategies. We employed a liquid biopsy approach to detect and characterize circulating PCs across disease states in 68 patients (MGUS = 11, SMM = 21, NDMM = 19, RRMM = 17) using multi-channel immunofluorescence staining and machine learning-assisted rare event detection. PCs were identified by CD138 and B-cell maturation antigen (BCMA) expressions, with distinct phenotypic subpopulations stratifying disease states. The D | CD138 | BCMA-Memb phenotype was the most predictive, with incidence increasing from MGUS to SMM and overt MM (p < 0.005). Multivariate modeling distinguished precursors from overt disease with 86% accuracy. Shifts in BCMA and CD45 expression suggested immune cell profile alterations with progression and treatment. These findings underscore PB-based liquid biopsy as a promising tool for MM detection and monitoring, revealing circulating PC heterogeneity.
Shishido et al. (Tue,) studied this question.