Zeitschrift für medizinische Diagnosemethoden
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Clinical Applications of Molecular Technologies in Hematology

Esther Pui-Ting Lam, Charles Ming-Lok Chan, Nancy Bo-Yin Tsui1, Thomas Chi-Chuen Au, Kit-Fai Wong, Heong-Ting Wong, Ka-Yue Chiu, Lawrence Wing-Chi Chan, Benjamin Yat-Ming Yung and Sze-Chuen Cesar Wong,

The rapid emergence of molecular diagnostic platforms has revolutionized the diagnostic approaches in hematology laboratory. Fluorescence in-situ hybridization, polymerase chain reaction and DNA sequencing are common techniques used in routine clinical laboratories for the diagnosis of hematological diseases. Different molecular techniques are indicated in different situations. This paper describes the utility of common molecular techniques. Fluorescence in-situ hybridization is specific for detection of chromosomal abnormalities using fluorescent labeled targeting probe. Polymerase chain reaction amplifies target DNA and reverse transcription polymerase chain reaction amplifies target RNA for the analysis of gene and its expression level. Real-time polymerase chain reaction is highly sensitive for detection of minimal residual disease in hemic malignancies. Gap-polymerase chain reaction is often employed for diagnosis of large deletions such as in alpha thalassemia. Allele-specific polymerase chain reaction is commonly used for single nucleotide polymorphism detection which is common in beta thalassemia, myeloproliferative neoplasm and acute leukemia. Inverse shifting-polymerase chain reaction can be employed for the detection of large genetic rearrangements such as those seen in hemophilia A. For genetically complex diseases such as hemophilia A, which involves a great variety of mutations in large genes, high resolution melting analysis can be used to scan for point mutations. Any suspected mutations are confirmed using post-PCR technologies, such as DNA sequencing. Although conventional diagnostic methods are able to provide a basic analysis in most cases, molecular technologies generate valuable genetic information that can refine diagnosis, better predict prognosis and facilitate disease monitoring.