The Matrix Metalloproteinase-9/Neutrophil Gelatinase-Associated Lipocalin Complex Plays a Role in Breast Tumor Growth and Is Present in the Urine of Breast Cancer Patients

Abstract

Having previously shown that the binding of neutrophil gelatinase-associated lipocalin (NGAL) to matrix metalloproteinase-9 (MMP-9) protects this extracellular matrix remodeling enzyme from autodegradation, we hypothesized that the addition of NGAL to breast cancer cells, which do not express this protein but do express MMP-9, might result in a more aggressive phenotype in vivo. Based on our previous reports that MMPs can be detected in the urine of cancer patients, we also asked whether MMP-9/NGAL could be detected in the urine of breast cancer patients and whether it might be predictive of disease status. Clones of MCF-7 human breast cancer cells differentially expressing NGAL were generated by stable transfection with human NGAL expression constructs. The established clones were then implanted s.c. in immunodeficient mice and tumor growth was monitored. In addition, we analyzed the urine of individuals with breast cancer and age-matched, sex-matched controls using gelatin zymography for the presence of MMP-9/NGAL. Increased NGAL expression resulted in significant stimulation of tumor growth. Immunohistochemical analysis of MCF-7 tumors revealed that the NGAL-overexpressing ones exhibited increased growth rates that were accompanied by increased levels of MMP-9, increased angiogenesis, and an increase in the tumor cell proliferative fraction. In addition, MMP-9/NGAL complex was detected in 86.36% of the urine samples from breast cancer patients but not in those from healthy age and sex-matched controls. These findings suggest, for the first time, that NGAL may play an important role in breast cancer in vivo by protecting MMP-9 from degradation thereby enhancing its enzymatic activity and facilitating angiogenesis and tumor growth. Clinically, these data suggest that the urinary detection of MMP-9/NGAL may be useful in noninvasively predicting disease status of breast cancer patients.