Breast cancer is the most common cancer in women. In the Western world, approximately one in eight women will develop an invasive breast carcinoma in her lifetime. While the disease is curable in early stages, about 50% of the patients have stage II or III tumors and are potential candidates for systemic therapy. This patient group would benefit from a patient-tailored therapy on the basis of biomarker testing.

While genetic alterations have been extensively characterized in breast cancer, the changes in metabolism that occur downstream from genomic and proteomic alterations have not been analyzed in detail to date.

The metabolome reflects alterations in the pathophysiological state of biological systems. Metabolic alterations can be the consequences of changes in metabolic pathways, but also in signalling pathways, membrane turnover and other cellular networks. Since small changes in enzyme concentrations or activities can lead to large changes in metabolite levels, the metabolome is regarded as the amplified output of a biological system.

The METAcancer objective is to test the hypothesis, that alterations in the level of metabolites can be used for a molecular classification of breast cancer as well as for the identification of new prognostic and predictive biomarkers.

The concept of the METAcancer approach is the application of combined technologies for metabolic profiling to large-scale analysis of patient samples in the field of translational research in breast cancer:

• Our project is based on a large tumor biobank as well as on comprehensive previous investigations of the consortium partners.
  
  
• We will use three different metabolic profiling technologies (GC-MS, NMR and LC-MS) to maximize the coverage of the breast cancer metabolome and apply advanced strategies for the identification of individual metabolites.
  
  
• METAcancer's integrated data-mining approach combines metabolomic data gained under the project with existing transcriptomic data pools for bioinformatic interpretation of cellular networks.

By this strategy, we will be able to go beyond the metabolite level and to identify and validate selected protein and mRNA biomarkers relevant for metabolic alterations. This will result in a combined signature consisting of metabolites as well as key protein and mRNA markers as a basis for a validated diagnostic system to assess prognosis and to guide targeted therapies in breast cancer.