We examined a series of 667 patients with node-negative breast carcinomas in order to identify prognostic immunohistochemical molecular signatures for the prediction of early metastasis, and potential new therapeutic targets. We used a standardized quantitative immunocytochemical approach with 37 antibodies, based on high-throughput tissue microarrays and image analysis, and analyzed the results with respect to metastatic status after a mean follow-up of 86 months. Complete data were obtained for 586 patients. The predictive value of the markers was first analyzed individually by univariate analysis (log rank test) in 586 node-negative tumors, according to metastatic status during follow-up. Twenty-seven markers had significant prognostic value. ROC curve analysis (logistic regression) was then used to determine the marker combination that best classified the patients with and without metastases. A 15-marker signature (Bcl2, P16, P21, P27, P53, CD34, CA IX, c-kit, FGF-R1, P38, JAK, pSTAT3, CK9, STAT1 and ER) correctly classified 84.8% of the patients (sensitivity 85.5%, specificity 84.6%). When ER, PR and c-erb B2 were excluded from the analysis, a very similar signature, in which CK8-18 replaced ER, correctly classified 83.6% of the patients (sensitivity 84.5%, specificity 83.4%). These results show that quantitative immunoprofiling, independently of ER, PR and c-erb B2 status, can provide a basal-like signature, can properly predict the metastatic risk of node-negative breast carcinomas at diagnostic time. This may be helpful for selecting patients who do not need aggressive adjuvant chemotherapy, and for developing tailored therapies.