Masters theses : Medical Laboratory Science

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    The Role of Non-lineage Markers: HLA-Dr, CD34 and CD45 in Differentiation Between Acute Myeloid Leukemia (AML) and Acute Lymphoid Leukemia (ALL) Using Flow Cytometry among Sudanese Patients
    (Al-Neelain University, 2020) Tagreed Ahmed Elhadi Abd El Rahim
    Abstract  Introduction: Acute leukemias are different cancers; occur in bone marrow (BM) responsible for supplying blood cells. It develops and progress quickly and therefore needs to be treated as soon as they are diagnosed, it affects very immature blood cells, preventing them from maturing properly, it characterized by abnormal and uncontrolled growth of blood-forming cells. They are classified into two subclasses; Acute Lymphoblastic Leukemia (ALL) and Acute Myeloid Leukemia (AML) based on morphologic, cytochemical and immunophenotypic features. Recent world health organization (WHO ( classification of acute leukemias incorporates non-morphologic data, including Flow Cytometry Immunophenotyping (FCMI), karyotypic, and molecular data. Here we concentrate on Flow cytometry (FCM) which is the one of the most advance and accurate techniques. It is an analytical method used for measurements of light scattering and emission of fluorescence produced by cells and chromosomes. It is a laser-based technology used to measure characteristics of biological particles. In this descriptive study we use the advance properties to characterize leukemic cell to establish and differentiate lineage such as AML and ALL in Sudanese patients in period: Jan/2015 – Feb/2017.  CD45 marker was included in immunophenotypic panel to differentiate the blast cells and help to detect them in heterogenous population, since almost all leukemic cell populations exhibit decreased (dim) CD45 expression compared to normal leukocytes. Non-lineage markers CD45, CD34 and HLA-Dr and their indices: percent (%), mean fluorescence intensity (MnI) and peak width (PW) were studied. I Methodology: One hundred and fifteen samples were collected. 53.9% were males, 46.1% were females. In terms of age groups: 49.5% were children and 50.4% were adults. The BM samples were 67.8% and PB samples were 32.2%. BM samples were filtered, monoclonal antibodies (McAbs): CD45, HLA-Dr and CD34 were added to all samples, lysing solution was added, and all tubes were acquisition in FCM. Twenty samples, ten BM and ten peripheral blood (PB) samples, withdrawn from each patient 2 samples were taken, and processed as the previous samples. CD45/SSC, forward scatter (FSC) and side scatter (SSC) were applied in data analysis to identify blast cells specifically and exclude the debris, doublets and red blood cells (RBCs) ruminant. Data was analysed in EPICS XL Beckman Coulter, using software system II.  Results: The frequency of leukemia subtypes were as follow: AML- M0: 10%, AML-M1: 3%, AML-M2: 16%, AML-M3: 4%, AML-M4: 7%, AML-M5: 5%, AML-M7: 2%, B-ALL: 44% and T-ALL: 9%. The result of CD45, HLA-Dr and CD34 with their indices, FSC, SSC and CD45/SSC parameters were collected, and the data was interpreted. The findings of statistical analysis as follow: CD45% P.value .276, HLA-Dr% P.value .000, CD34% P.value.000. CD45 MIN P.value.000, HLA-Dr MIN P.value .504, CD34 MIN P.value .625. CD45 PW P.value .000, HLA-Dr PW P.value .000, CD34 PW P.value .322. FSC P.value .002, SSC P.value .000. CD45 against (SSC CD45/SSC) P.value .001. Results of twenty samples were collected and data were analysed as follows: comparison between BM and PB samples showed that the mean expression of CD45 was: 71.8% and 69.9% respectively with P.value of .361.The mean expression of HLA-Dr was: 63.1% and 67.5% respectively with P.value of .741. II‫‪with‬‬ ‫‪P.value‬‬ ‫‪of‬‬ ‫‪.663.‬‬ ‫‪‬‬ ‫‪Conclusion:‬‬ ‫‪In‬‬ ‫‪this‬‬ ‫‪study‬‬ ‫‪introduction‬‬ ‫‪of‬‬ ‫‪non-lineage‬‬ ‫‪marker‬‬ ‫‪and‬‬ ‫‪their‬‬ ‫‪indices‬‬ ‫‪utilize‬‬ ‫‪for‬‬ ‫‪rapid‬‬ ‫‪characterization‬‬ ‫‪of‬‬ ‫‪blast‬‬ ‫‪cells‬‬ ‫‪and‬‬ ‫‪classification‬‬ ‫‪of‬‬ ‫‪AML,‬‬ ‫‪ALL‬‬ ‫‪and‬‬ ‫‪their‬‬ ‫‪subtypes,‬‬ ‫‪also‬‬ ‫‪it‬‬ ‫‪lead‬‬ ‫‪to‬‬ ‫‪minimize‬‬ ‫‪the‬‬ ‫‪patient‬‬ ‫‪cost.‬‬ ‫‪Comparison‬‬ ‫‪made‬‬ ‫‪between‬‬ ‫‪this‬‬ ‫‪study‬‬ ‫‪and‬‬ ‫‪other‬‬ ‫‪studies‬‬ ‫‪done‬‬ ‫‪in‬‬ ‫‪other‬‬ ‫‪countries,‬‬ ‫‪we‬‬ ‫‪found‬‬ ‫‪that‬‬ ‫‪the‬‬ ‫‪results‬‬ ‫‪were‬‬ ‫‪not‬‬ ‫‪differing,‬‬ ‫‪but‬‬ ‫‪they‬‬ ‫‪are‬‬ ‫‪close‬‬ ‫‪to‬‬ ‫‪each‬‬ ‫‪other.‬‬