Breast cancer is the most common cancer in women. It is therefore in the focus of approaches for the identification of predictive molecular biomarkers. In contrast to transcriptomics, the changes in metabolite levels that are associated with tumor development and progression have not been investigated to a great extent, so far. Metabolomics is defined as the study of all metabolites in a cell, tissue or organism, and the metabolome is regarded as the amplified output of a biological system. The originality of the METAcancer approach will be the first-time application of combined technologies for metabolic profiling to large-scale analysis of patient samples in the field of translational research in breast cancer. We will test the hypothesis that alterations in the level of metabolites can be used for a molecular classification of breast cancer and for identification of new prognostic and predictive biomarkers. Our project is based on a large tumor biobank as well as on 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 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.