The exponentially increasing volume of information extracted from genomic and proteomic applications on cancer, while providing new insights into molecular composition of cancer cells and tissues, imposes new challenges on data rationalization as a tool for clinically relevant biomarker discovery. Breast cancer represents the most frequent and potentially aggressive type of cancer and, moreover, it is one of the most enigmatic and poorly predictable in its evolution, likely because it includes several different forms that behave differently among patients. Current clinical parameters for breast cancer diagnosis and cure are: tumour size, axillary lymph node status, histological grading and presence or absence of metastases. Prognostic/predictive properties, such as oestrogen and progesterone receptor status, and human epidermal growth factor receptor (HER-2/neu) status are currently used for therapeutic decision. Conversely, it is now emerging that the number of genetic mutations and epigenetic deregulations in cancer is far more higher than previously thought. Therefore, proteomic screening for differential protein expression in subsets of tumor samples is an essential tool to generate data bases, to contribute to the knowledge of biological pathways in a given cancer tissue, to allow a molecular classification of cancer for patient stratification and biomarker discovery.To minimize the limitation induced by heterogeneity of breast cancer tissues, we have recently introduced, as objective criterion to compare proteomes of different tissues, the normalization of the expression levels of individual proteins, for actin content in each tissue extract (Pucci-Minafra I. et al., 2008). With the aim to identify candidate markers for stratification of patients and clinical correlations, in the present study we evaluated the surgical tissues proteomic profiles of an increased number of patients diagnosed for ductal infiltrating breast cancer. Differentially expressed proteins were identified by 2D-IPG coupled with Peptide Mass Fingerprint (PMF) and N-terminal microsequencing.
|Number of pages||1|
|Publication status||Published - 2008|