Use of the Cell-Dyn 3500 to predict leukemic cell lineage in peripheral blood of dogs and cats.
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.
Fernandes
Peter J
PJ
Department of Pathobiology, College of Veterinary Medicine, Texas A&M University College Station, USA.
Modiano
Jaime F
JF
Wojcieszyn
John
J
Thomas
Jennifer S
JS
Benson
Patricia A
PA
Smith
Roger
R
Avery
Anne C
AC
Burnett
Robert C
RC
Boone
Laura I
LI
Johnson
Mark C
MC
Pierce
Kenneth R
KR
D000465:Algorithms; D000818:Animals; D000941:Antigens / Q000235:genetics; D002371:Cat Diseases / Q000175:diagnosis / Q000473:pathology*; D002415:Cats; D019070:Cell Lineage; D004283:Dog Diseases / Q000175:diagnosis / Q000473:pathology*; D004285:Dogs; D005260:Female; D005434:Flow Cytometry; D015870:Gene Expression; D006405:Hematology / Q000295:instrumentation*; D007150:Immunohistochemistry; D007938:Leukemia / Q000145:classification / Q000175:diagnosis / Q000473:pathology* / Q000662:veterinary*; D008297:Male; D010641:Phenotype
D000941:Antigens