Flow cytometry for fast screening and automated risk assessment in systemic light-chain amyloidosis
Keywords: 
Minimal residual disease
Adverse prognostic-factor
Diagnosed AL amyloidosis
Differential-diagnosis
Translocation (11/14)
Multiple-myeloma
CD43 expression
Staging system
Plasma-cells
Identification
Issue Date: 
2019
Publisher: 
Nature
OpenAIRE: 
info:eu-repo/grantAgreement/EC/ERC/680200-MYELOMANEXT
ISSN: 
0887-6924
Citation: 
Puig, N. (Noemí); Paiva, B. (Bruno); Lasa, M. (Marta); et al. "Flow cytometry for fast screening and automated risk assessment in systemic light-chain amyloidosis". Leukemia. 33 (5), 2019, 1256 - 1267
Abstract
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.

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