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
Department of Pathobiology, College of Veterinary Medicine, Texas A&M University College Station, USA.
Vet Clin Pathol. 2002;31(4):167-82. doi: 10.1111/j.1939-165x.2002.tb00298.x.
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.
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.
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.
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.
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.
形态学和细胞化学是犬猫白血病分类的基础。自动化血液学检测仪器、免疫表型分析、细胞遗传学和分子生物学的进展显著提高了我们识别和分类自发性骨髓增殖性和淋巴细胞增殖性疾病的能力。
本研究的目的是评估Cell-Dyn 3500(CD3500)自动化血液分析仪提供的基于流式细胞术的光散射模式在预测犬猫外周血白血病细胞谱系方面的效用。
根据CD3500全血细胞计数输出数据,采用一种算法对15只犬和6只猫的白血病细胞进行初步分类,随后通过酶细胞化学、免疫细胞化学、间接流式细胞术和抗原受体基因重排分析对其进行表型分析。
该算法对21只动物中19只的白血病细胞起源(红系/巨核系潜能、髓系白血病、单核细胞白血病、慢性粒细胞白血病、淋巴细胞白血病)做出了正确预测。白细胞阻抗计数和白细胞光学计数的不匹配,以及血液中原始细胞的显微镜评估,有助于预测具有红系或巨核系潜能的骨髓增殖性疾病。白细胞光散射模式能够区分髓系白血病(以急性粒单核细胞白血病、急性单核细胞白血病、慢性粒细胞白血病为代表)和淋巴细胞白血病(包括急性和慢性淋巴细胞白血病)。1例急性淋巴细胞白血病被误诊为慢性淋巴细胞白血病。
算法分析可应用于CD3500生成的数据,以预测犬猫外周血白血病细胞的起源。这种快速定量技术可用于改善诊断决策、扩大治疗选择并提高预后准确性。