Use of the Cell-Dyn 3500 to predict leukemic cell lineage in peripheral blood of dogs and cats |
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| Authors: | Fernandes Peter J Modiano Jaime F Wojcieszyn John Thomas Jennifer S Benson Patricia A Smith Roger Avery Anne C Burnett Robert C Boone Laura I Johnson Mark C Pierce Kenneth R |
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| Institution: | From the Department of Pathobiology, College of Veterinary Medicine, Texas A&M University, College Station, Tex (Fernandes, Modiano, Wojcieszyn, Thomas, Benson, Smith, Boone, Johnson, Pierce);;Department of Physiological Sciences, College of Veterinary Medicine, University of Florida, Gainesville, Fla;Center for Cancer Causation and Prevention, AMC Cancer Research Center, Denver, Colo;Department of Pathology, College of Veterinary Medicine, Colorado State University, Fort Collins, Colo |
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| Abstract: | Background— Morphology and cytochemistry are the foundation for classification of leukemias in dogs and cats. Advances in automated hematology instrumentation, immunophenotyping, cytogenetics, and molecular biology are significantly improving our ability to recognize and classify spontaneous myeloproliferative and lymphoproliferative disorders. Objective— The purpose of this study was to assess the utility of flow cytometry‐based light scatter patterns provided by the Cell‐Dyn 3500 (CD3500) automated hematology analyzer to predict the lineage of leukemic cells in peripheral blood of dogs and cats. Methods— Leukemic cells from 15 dogs and 6 cats were provisionally classified using an algorithm based on the CD3500 CBC output data and were subsequently phenotyped by enzyme cytochemistry, immunocytochemistry, indirect flow cytometry, and analysis of antigen receptor gene rearrangement. Results— The algorithm led to correct predictions regarding the ontogeny of the leukemic cells (erythroid/megakaryocytic potential, myeloid leukemia, monocytic leukemia, chronic granulocytic leukemia, lymphoid leukemia) in 19/21 animals. Mismatches in the WBC impedance count and the WBC optical count in conjunction with microscopic assessment of blasts in the blood were useful for predicting myeloproliferative disorders with erythroid or megakaryocytic potential. The leukocyte light scatter patterns enabled distinction among myeloid leukemias (represented by acute myelomonocytic leukemia, acute monocytic leukemia, chronic granulocytic leukemia) and lymphocytic leukemias (including acute and chronic lymphocytic leukemias). One case of acute lymphocytic leukemia was misidentified as chronic lymphocytic leukemia. Conclusions— Algorithmic analyses can be applied to data generated by the CD3500 to predict the ontogeny of leukemic cells in the peripheral blood of dogs and cats. This rapid and quantitative technique may be used to improve diagnostic decisions, expand therapeutic choices, and increase prognostic accuracy. |
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| Keywords: | Cell-Dyn 3500 automated hematology analyzer flow cytometry leukemia light scatter patterns lymphoma pheno-typing |
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