Flow cytometry for fast screening and automated risk assessment in systemic light-chain amyloidosis
Puig N (1), Paiva B (2), Lasa M (3), Burgos L (3), Perez JJ (1), Merino J (3), Moreno C (3), Vidriales MB (1), Toboso DG (1), Cedena MT (4), Ocio EM (1), Lecumberri R (3), García de Coca A (5), Labrador J (6), Gonzalez ME (7), Palomera L (8), Gironella M (9), Cabañas V (10), Casanova M (11), Oriol A (12), Krsnik I (13), Pérez-Montaña A (14), de la Rubia J1 (5), de la Puerta JE (16), de Arriba F (17), Prosper F (3), Martinez-Lopez J (4), Lecrevisse Q (18), Verde J (19), Mateos MV (3), Lahuerta JJ (4), Orfao A (18), San Miguel JF (3).
Early diagnosis and risk stratification are key to improve outcomes in light-chain (AL) amyloidosis. Here we used multidimensional-flow-cytometry (MFC) to characterize bone marrow (BM) plasma cells (PCs) from a series of 166 patients including newly-diagnosed AL amyloidosis (N = 94), MGUS (N = 20) and multiple myeloma (MM, N = 52) vs. healthy adults (N = 30). MFC detected clonality in virtually all AL amyloidosis (99%) patients.
Furthermore, we developed an automated risk-stratification system based on BMPCs features, with independent prognostic impact on progression-free and overall survival of AL amyloidosis patients (hazard ratio: ≥ 2.9;P ≤ .03).
Simultaneous assessment of the clonal PCs immunophenotypic protein expression profile and the BM cellular composition, mapped AL amyloidosis in the crossroad between MGUS and MM; however, lack of homogenously-positive CD56 expression, reduction of B-cell precursors and a predominantly-clonal PC compartment in the absence of an MM-like tumor PC expansion, emerged as hallmarks of AL amyloidosis (ROC-AUC = 0.74;P < .001), and might potentially be used as biomarkers for the identification of MGUS and MM patients, who are candidates for monitoring pre-symptomatic organ damage related to AL amyloidosis.
Altogether, this study addressed the need for consensus on how to use flow cytometry in AL amyloidosis, and proposes a standardized MFC-based automated risk classification ready for implementation in clinical practice.