Related Experiment Video For CD markers
Updated: May 4, 2026

HPLC-based Assay to Monitor Extracellular Nucleotide/Nucleoside Metabolism in Human Chronic Lymphocytic Leukemia Cells
Published on: July 20, 2016
Optimizing Markers for Chronic Lymphocytic Leukemia Diagnosis by Flow Cytometry: Results From the Nationwide Thai
Ornnicha Sathitakorn1, Sirorat Kobbuaklee1, Chantiya Chanswangphuwana1
1Department of Medicine, Faculty of Medicine, Chulalongkorn University and King Chulalongkorn Memorial Hospital, Bangkok, Thailand.
Introduction:
Accurate diagnosis of chronic lymphocytic leukemia (CLL) is crucial for effective management. Although recent advancements in flow cytometry have significantly improved diagnostic accuracy, distinguishing atypical cases of CLL remains challenging. This study aimed to identify specific markers to enhance CLL diagnosis and prognostic assessment.
Methods:
Blood or bone marrow from patients presenting with persistent lymphocytosis (> 5 × 103/μL) was collected. Flow cytometric analysis was performed using a customized CLL panel to evaluate the expression of CD markers for the diagnosis of CLL. Clinical and laboratory data were reviewed to support the CLL diagnosis.
Results:
A total of 228 patients were included in the study, including 206 patients (90.4%) in the CLL group and 22 patients (9.6%) in the non-CLL group. The use of CD5, CD19, and CD200 in combination for diagnosing CLL demonstrated a sensitivity of 82.5% (95% CI, 76.6-87.4) and a specificity of 90.9% (95% CI, 70.8-98.9). In the final predictive model, which incorporated five markers (CD5, CD19, CD200, CD31, and CD11c), both specificity and positive predictive value for CLL diagnosis were close to 100% (95% CI, 84.6-100 and 95.5-100, respectively). CD38 was identified as an independent predictor of increased relapse risk, while LAIR expression was associated with a reduced relapse rate.
Conclusion:
Expression of CD5, CD19, and CD200 by flow cytometry demonstrated a highly sensitive and accurate diagnostic utility for CLL. Adding both CD31 and CD11c to the predictive model further improved diagnostic specificity, reaching as high as 100%. The identification of relapse-associated markers provides valuable insights for personalized treatment strategies.
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