Literature DB >> 27479652

FLOCK cluster analysis of plasma cell flow cytometry data predicts bone marrow involvement by plasma cell neoplasia.

David M Dorfman1, Charlotte D LaPlante2, Betty Li2.   

Abstract

We analyzed plasma cell populations in bone marrow samples from 353 patients with possible bone marrow involvement by a plasma cell neoplasm, using FLOCK (FLOw Clustering without K), an unbiased, automated, computational approach to identify cell subsets in multidimensional flow cytometry data. FLOCK identified discrete plasma cell populations in the majority of bone marrow specimens found by standard histologic and immunophenotypic criteria to be involved by a plasma cell neoplasm (202/208 cases; 97%), including 34 cases that were negative by standard flow cytometric analysis that included clonality assessment. FLOCK identified discrete plasma cell populations in only a minority of cases negative for involvement by a plasma cell neoplasm by standard histologic and immunophenotypic criteria (38/145 cases; 26%). Interestingly, 55% of the cases negative by standard analysis, but containing a FLOCK-identified discrete plasma cell population, were positive for monoclonal gammopathy by serum protein electrophoresis and immunofixation. FLOCK-identified and quantitated plasma cell populations accounted for 3.05% of total cells on average in cases positive for involvement by a plasma cell neoplasm by standard histologic and immunophenotypic criteria, and 0.27% of total cells on average in cases negative for involvement by a plasma cell neoplasm by standard histologic and immunophenotypic criteria (p<0.0001; area under the curve by ROC analysis=0.96). The presence of a FLOCK-identified discrete plasma cell population was predictive of the presence of plasma cell neoplasia with a sensitivity of 97%, compared with only 81% for standard flow cytometric analysis, and had specificity of 74%, PPV of 84% and NPV of 95%. FLOCK analysis, which has been shown to provide useful diagnostic information for evaluating patients with suspected systemic mastocytosis, is able to identify neoplastic plasma cell populations analyzed by flow cytometry, and may be helpful in the diagnostic evaluation of bone marrow samples for involvement by plasma cell neoplasia.
Copyright © 2016 Elsevier Ltd. All rights reserved.

Entities:  

Keywords:  Bone marrow biopsy; Cluster analysis; Flow cytometry; Monoclonal gammopathy; Multiple myeloma

Mesh:

Year:  2016        PMID: 27479652     DOI: 10.1016/j.leukres.2016.07.003

Source DB:  PubMed          Journal:  Leuk Res        ISSN: 0145-2126            Impact factor:   3.156


  3 in total

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Authors:  Richard H Scheuermann; Jack Bui; Huan-You Wang; Yu Qian
Journal:  Clin Lab Med       Date:  2017-12       Impact factor: 1.935

2.  K-means quantization for a web-based open-source flow cytometry analysis platform.

Authors:  Nathan Wong; Daehwan Kim; Zachery Robinson; Connie Huang; Irina M Conboy
Journal:  Sci Rep       Date:  2021-03-24       Impact factor: 4.379

Review 3.  Application of Machine Learning for Cytometry Data.

Authors:  Zicheng Hu; Sanchita Bhattacharya; Atul J Butte
Journal:  Front Immunol       Date:  2022-01-03       Impact factor: 7.561

  3 in total

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