| Literature DB >> 30542145 |
Noemi Puig1, Bruno Paiva2, Marta Lasa3, Leire Burgos3, Jose J Perez1, Juana Merino3, Cristina Moreno3, Maria-Belen Vidriales1, Dolores Gómez Toboso1, Maria-Teresa Cedena4, Enrique M Ocio1, Ramon Lecumberri3, Alfonso García de Coca5, Jorge Labrador6, Maria-Esther Gonzalez7, Luis Palomera8, Mercedes Gironella9, Valentin Cabañas10, Maria Casanova11, Albert Oriol12, Isabel Krsnik13, Albert Pérez-Montaña14, Javier de la Rubia15, Jose-Enrique de la Puerta16, Felipe de Arriba17, Felipe Prosper3, Joaquin Martinez-Lopez4, Quentin Lecrevisse18, Javier Verde19, Maria-Victoria Mateos3, Juan-Jose Lahuerta4, Alberto Orfao18, Jesús F San Miguel3.
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.Entities:
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Year: 2018 PMID: 30542145 DOI: 10.1038/s41375-018-0308-5
Source DB: PubMed Journal: Leukemia ISSN: 0887-6924 Impact factor: 11.528