Matrix metalloproteinase (MMP) 2 and 9 are involved in cancer invasion and metastasis, and increased levels occur in serum and plasma of breast cancer (BC) patients. It is, however, unclear whether changes in serum levels can be exploited for early detection or classification of patients into different risk/disease categories. In our study, we measured concentration and activity of MMP2/9 in sera of 345 donors classified as low risk (Gail score < 1.7), high risk (HR) (Gail score 1.7), benign disease or BC. Kruskal-Wallis and Mann-Whitney nonparametric tests showed that total-MMP2 concentration is higher in HR compared to control (p 5 0.012), benign (p 5 0.001) and cancer (p 5 0.007). Active MMP2 (aMMP2) concentration is higher in control than benign and cancer (p < 0.001, respectively). Total and aMMP9 concentrations are higher in cancer than benign (p < 0.001, p 5 0.002, respectively). Total-MMP2 and total-MMP9 activities are lower in control than benign (p < 0.001, p 5 0.002, respectively) and cancer (p < 0.001, respectively). Total-MMP2 and MMP9 activities are also higher in cancer than benign (p 5 0.004, p < 0.001) and HR (p 5 0.008, p 5 0.007, respectively). These results were not affected by age or inclusion/exclusion of donors with noninvasive cancer or atypical hyperplasia. Linear discriminant analysis revealed that HR donors are characterized by lower total-MMP2 and higher aMMP2. Overall group classification accuracy was 64.5%. Independent validation based on the leave-one-out cross validation approach gave an overall classification of 63%. Our study provides evidence supporting the potential role of serum MMP2/9 as biomarkers for breast disease classification. ' 2006 Wiley-Liss, Inc.Key words: serum profiling; matrix metalloproteinase 2 and 9; breast cancer; breast disease; high risk; Gail model Matrix metalloproteinases (MMPs) are a family of highly homologous, zinc-and calcium-dependent extracellular enzymes classified into 5 groups (collagenases, gelatinases, stromelysin, matrilysin and the membrane-type MMP) based on substrate specificity, protein domain structure, sequence homology and ability/ inability to be secreted.

The most important predictor of prognosis in breast cancer is lymph node status, yet little is known about molecular changes associated with lymph node metastasis. Here, gene expression analysis was performed on primary breast (PBT) and corresponding metastatic lymph node (MLN) tumors to identify molecular signatures associated with nodal metastasis. RNA was isolated after laser microdissection from frozen PBT and MLN from 20 patients with positive lymph nodes and hybridized to the microarray chips. Differential expression was determined using Mann-Whitney testing; Bonferroni corrected P values of 0.05 and 0.001 were calculated. Results were validated using TaqMan assays. Fifty-one genes were differentially expressed (P < 1 x 10(-5), less than twofold differences) between the PBT and paired MLN; 13 with significantly higher expression in the MLN and 38 in the PBT. qRT-PCR validated the differential expression of 40/51 genes. Of the 40 validated genes, NTS and PAX5 were found to have >100-fold higher expression in MLT while COL11A1, KRT14, MMP13, TAC1 and WNT2 had >100-fold higher expression in PBT. Gene expression differences between PBT and MLN suggests that expression of a unique set of genes is required for successful lymph node colonization. Genes expressed at higher levels in PBT are involved in degradation of the extracellular matrix, enabling cells with metastatic potential to disseminate, while genes expressed at higher levels in metastases are involved in transcription, signal transduction and immune response, providing cells with proliferation and survival advantages. These data improve our understanding of the biological processes involved in successful metastatis and provide new targets to arrest tumor cell dissemination and metastatic colonization.

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We developed a quality assurance (QA) tool, namely microarray outlier filter (MOF), and have applied it to our microarray datasets for the identification of problematic arrays. Our approach is based on the comparison of the arrays using the correlation coefficient and the number of outlier spots generated on each array to reveal outlier arrays. For a human universal reference (HUR) dataset, which is used as a technical control in our standard hybridization procedure, 3 outlier arrays were identified out of 35 experiments. For a human blood dataset, 12 outlier arrays were identified from 185 experiments. In general, arrays from human blood samples displayed greater variation in their gene expression profiles than arrays from HUR samples. As a result, MOF identified two distinct patterns in the occurrence of outlier arrays. These results demonstrate that this methodology is a valuable QA practice to identify questionable microarray data prior to downstream analysis.

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