Detection of Minimal Residual Disease in the Peripheral Blood of Breast Cancer Patients, with a Multi Marker (MGB-1, MGB-2, CK-19 and NY-BR-1) Assay

采用多标志物(MGB-1、MGB-2、CK-19 和 NY-BR-1)检测法检测乳腺癌患者外周血中的微小残留病灶

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Abstract

PURPOSE: Minimal residual disease (MRD) refers to micrometastases that are undetectable by conventional means and is a potential source of disease relapse. This study aimed to detect the presence of breast cancer (BC) biomarkers (MGB-1, MGB-2, CK-19, NY-BR-1) using real-time polymerase chain reaction (RT-PCR) in peripheral blood mononuclear cells (PBMC) of BC patients and the impact of a positive assay on clinical outcome. PATIENTS AND METHODS: Patients in the analysis included females >18 years of age with biopsy-proven carcinoma of the breast. A 10 mL sample of venous blood was obtained from 10 healthy controls and 25 breast cancer patients. Comparisons of peripheral blood markers were made with clinicopathological variables. RESULTS: High-quality RNA was extracted from all samples with a mean RNA concentration of 224.8±155.3 ng/µL. Each of the molecular markers examined was highly expressed in the primary breast tumors (n = 3, positive controls) with none of the markers detected in healthy negative controls. The NY-BR-1 marker was expressed in one (4%) patient with metastatic disease with no MGB-1 and MGB-2 detected in any sample derived from the study patients. The CK-19 marker was detected in 16 (64%) of the BC cases. No correlation was found between CK-19 expression and tumor stage (P = 0.07) or nodal status (P = 0.32). No correlation was identified in the BC patients between CK-19 expression and receptor status in the BC primary tumor. CONCLUSION: This study showed high expression of all 4 markers NY-BR-1, MGB-1, MGB-2 and CK-19 in the PBMCs derived from breast cancer patients. CK-19 was detected in 64% of the stage I-III cases operated with curative intent, the only recurrent events occurring in the CK-19-positive cases. Our data confirm the need to enhance techniques for detection of MRD, which may better predict patients at risk for relapse.

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