Expression profiling and biomarker(s) discovery aim to provide means for tumour diagnosis, classification, therapy response and prognosis. The identification of novel markers could potentially lead to the building of robust early detection strategies and personalized, effective breast cancer therapies that would improve patient outcome. Recent evidence supports the hypothesis that genomic expression profiling using microarray analysis is a reliable method for breast cancer classification and prognostication. However, genes clearly do not act by themselves, or indeed they do not have catalytic or signalling capabilities. Hence, genetic biomarker information alone cannot perfectly predict cancer and its response to treatment. Genes clearly exert their effect after transcription through translation into active proteins. Consequently, postgenomic projects correlating protein expression profiles with tumour classification have led to some established biomarkers. In this regard, these biomarkers associate with disease prediction and can be associated with treatment response. Recently, Brozokova and colleagues demonstrated that surface-enhanced laser desorption ionization time of flight mass spectrometry (SELDI-TOF MS) profiling of breast cancer tissue proteomes can potentially expand the biomarker repertoire and our knowledge of breast cancer behaviour.