Medüller, Lenfoepitelyoma Benzeri Ve İndiferansiye Gastrik Karsinomların Moleküler Ve Klinikopatolojik Özellikleri
Özet
Uner M., "Molecular and Clinicopathological Features of Medullary, Lymphoepithelioma Like and Undifferentiated Gastric Carcinomas", Hacettepe University School of Medicine, Pathology Department, Specialty Thesis, Ankara, 2015. Gastric carcinomas (GC) are heterogeneous group of neoplasms with broad cytological and architectural variations. The purpose of this thesis is to further characterize rarely seen advanced GCs with medullary like features, lymphoepithelioma like tumors with inflammatory stroma and undifferentiated carcinomas with minimal or no glandular differentiation. 53 GCs which support the appropriate criteria were included to this study among 654 surgically resected gastric tumor cases, reported at the Pathology Department of Hacettepe University Medical Faculty between 2000-2015. All the slides of these cases were reviewed, also clinical and pathological data were recorded. A tissue microarray (TMA) block and a tissue block (TA) were constructed using paraffin embedded blocks of these cases with typical morphology, representing two cores for each tumor. Immunohistochemistry was performed to the serial sections from these TMA and TA blocks for E-cadherin, Beta-catenin, MUC5ac, p53, LMP1, CDX2, mismatch repair mechanism proteins (MLH1, PMS2, MSH2 and MSH6), C-erbB2, SALL4, AXIN, Ki-67 and Bcl-2 antibodies. Epstein-Barr Virus (EBV) status was evaluated by both EBER in situ hybridization and LMP1 immunohistochemistry. Morphological, immunohistochemical and molecular findings lead us to classify these 53 cases into medullary GC (29 cases with defective DNA mismatch repair mechanism), EBV associated GC (10 cases) and undifferantiated GC (10 cases). Also, clinicopathological features and survival data of 53 GCs were evaluated. In order to clarify the carcinogenesis of these rarely seen gastric carcinomas with hardly recognizable morphology and to apply alternative treatment strategies, biomarker supported classification algorithms will be more convenient.
