4.7 Article

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

Journal

LEUKEMIA
Volume 33, Issue 5, Pages 1256-1267

Publisher

NATURE PUBLISHING GROUP
DOI: 10.1038/s41375-018-0308-5

Keywords

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Funding

  1. Centro de Investigacion Biomedica en Red-Area de Oncologia-del Instituto de Salud Carlos III (CIBERONC) [CB16/12/00369, CB16/12/00400, CB16/12/00489]
  2. Instituto de Salud Carlos III/Subdireccion General de Investigacion Sanitaria [PI13/02196]
  3. Asociacion Espanola Contra el Cancer [GCB120981SAN]
  4. Black Swan Research Initiative of the International Myeloma Foundation
  5. European Research Council (ERC)

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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 = 9 4) , 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 MFCbased automated risk classification ready for implementation in clinical practice.

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