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Updated: Aug 14, 2026

Laser Microdissection-Based Protocol for the LC-MS/MS Analysis of the Proteomic Profile of Neuromelanin Granules
Published on: December 16, 2021
Free Light Chain Monomer-Dimer Pattern Analysis as Non-Invasive Tool in Predicting MGUS and SMM Progression
Avshalom Serok1, Lesya Olga Kukuy2, Omer Shaked3
1Department of Internal Medicine, Hadassah Medical Center, The Hebrew University of Jerusalem, Jerusalem 91120, Israel.
None:
Background/Objectives: MGUS and smoldering multiple myeloma (SMM) are precursor states of plasma cell disorders with variable risk of progression to multiple myeloma (MM). Yet, current risk stratification models combine clinical and laboratory parameters, including invasive bone marrow assessment, but have limited precision. Methods: We applied free light chain (FLC)-monomer (M)-dimer (D) pattern analysis (FLC-MDPA), a non-invasive Western blot-based serum assay, to detect abnormal FLC M-D patterns associated with early malignant transformation, for predicting progression in MGUS (n = 68) and SMM (n = 40). Among 96 patients with complete data, 50 formed a training set to define criteria for progressive disease and 46 comprised a validation set. Results: FLC-MDPA predicted biochemical progression (sensitivity 0.79, specificity 0.92, NPV 0.86; HR 19.96, 95% CI 4.31-92) and clinical progression (sensitivity 0.84, specificity 0.79; HR 13.5, 95% CI 3.68-49.64), with significantly higher progression rates in patients with abnormal patterns (p < 0.0001). Within this high-risk cohort, FLC-MDPA was associated with higher hazard ratios for both biochemical and clinical progression compared with the 2/20/20 model, while incorporation into a modified 2/20/MDPA model improved sensitivity and negative predictive value. In a multivariable model including MDPA and FLC ratio, MDPA was independently associated with both clinical (p = 0.001) and biochemical progression (p = 0.04). Conclusions: These findings suggest that FLC-MDPA is a promising non-invasive tool for risk stratification in MGUS and SMM, improving sensitivity and negative predictive value while potentially reducing reliance on bone marrow-based assessment.

