Evaluation of CD96 Expression in Acute Myeloid Leukemia Patients: Relation to Prognosis and Induction Chemotherapy

Abstract

Abstract Background In acute myeloid leukemia (AML), leukemic stem cells (LSCs) form a distinct group of leukemia cells showing stem cell-like features like self-renewal and proliferation. These potent cells significantly contribute to chemotherapy resistance, a major obstacle in AML therapy. LSCs express the characteristic phenotype CD34+/CD38- and have several specific markers, including CD33, CD96, CD123, CD90 and Death associated protein kinase (DAPK). Successful LSC eradication demands a combination of approaches, including targeting LSC-specific surface molecules. Objective To assess the expression of CD96 in AML patients at initial diagnosis and to correlate it with prognostic factors and response to induction chemotherapy. Materials and Methods This Case control study was conducted on 40 newly diagnosed AML patients in addition to 10 controls at Ain Shams University Hospitals between March 2022 and March 2023. Multicolor flowcytometry was employed to analyze CD96 expression in the CD34+/CD38- cell population of AML patients. The study further investigated the correlation between CD96 expression and both prognostic factors and response to induction chemotherapy. Results CD96 expression was significantly higher in AML patients group than control group (p-value = 0.008). CD96 was positive in 60% of AML patients and there was a highly significant association between CD96 expression and response to induction chemotherapy (p-value = <0.001). However, there was no statistically significant relation between CD96 expression with laboratory findings and standard prognostic factors. Conclusion Our study identified CD96 as a promising marker for LSCs in AML patients. Its frequent expression in the CD34+/CD38- LSC population and association with lower response rate to induction chemotherapy suggest its potential as a valuable tool for LSC identification, targeted therapy development and improved treatment outcome prediction in AML.