Scientific publications

Flow cytometry for fast screening and automated risk assessment in systemic light-chain amyloidosis

May 1, 2019 | Magazine: Leukemia

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.

CITATION  Leukemia. 2019 May;33(5):1256-1267. doi: 10.1038/s41375-018-0308-5. Epub 2018 Dec 12.

Our authors

Marta Lasa, investigadora del Grupo de Mieloma Múltiple del Cima Universidad de Navarra
Dr. Marta Lasa Ventura
Research Collaborator Multiple Myeloma Research Group
Leire Burgos Rodriguez
Laboratory technician Multiple Myeloma Research Group