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Flow Cytometric Immunophenotypic Analysis of 306 Cases of Multiple Myeloma

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Pei Yi Lin, Rebecca Owens, Guido J. K. Tricot, Carla S. Wilson·2004·American Journal of Clinical Pathology

Abstract

Bone marrow aspirates from 306 patients with multiple myeloma were analyzed by flow cytometric immunophenotyping. The plasma cells (PCs) were identified by their characteristic light scatter distribution and reactivity patterns to CD138, CD38, and CD45. Monoclonality was confirmed by immunoglobulin light chain analysis. The immunophenotypic profile of the PCs was determined with a panel of antibodies. Moderate to bright expression of CD56, CD117, CD20, CD45, and CD52 was detected in 71.7%, 17.8%, 9.3%, 8.8%, and 5.2% of cases, respectively. These antigens were expressed by a distinct subpopulation of the PCs in 6.3%, 2.2%, 3.7%, 2.9%, and 2.6% of additional cases. CD19 was negative in more than 99% of cases. The combination of CD38 and CD138 was superior to CD38 alone for identifying CD45+ myeloma and separating CD20+ myeloma from B-cell lymphoma. PC immunophenotyping might be useful for detecting minimal residual disease in cases with aberrant antigen expression and for selection of therapeutic agents that have specific membrane targets.

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Metadata

Authors
Lin, P. Y., Owens, R., Tricot, G. J. K., & Wilson, C. S.
Venue
American Journal of Clinical Pathology
Publisher
Oxford University Press
Published
2004-04-01
Volume
121
Issue
4
Pages
482-488
ISSN
0002-9173 (ISSN-L)
OpenAlex
W4234971900
Cited by
454
References
0
FWCI
6.04
Type
journal article
Language
en
Open access
bronze

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CoChat. (2026). Citation check: Flow Cytometric Immunophenotypic Analysis of 306 Cases of Multiple Myeloma. https://app.cochat.ai/papers/10.1309/74r4-tb90-buwh-27jx
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Lin, P. Y., Owens, R., Tricot, G. J. K., & Wilson, C. S. (2004). Flow Cytometric Immunophenotypic Analysis of 306 Cases of Multiple Myeloma. American Journal of Clinical Pathology. https://doi.org/10.1309/74r4-tb90-buwh-27jx

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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.