Breast cancer is an optimal "model disease" for studying personalized medicine. Breast cancer was the first malignancy for which a predictive factor forecasting response to therapy was identified nearly 50 years ago; the expression of the estrogen receptor (ER). Furthermore, breast cancer is by far the malignancy in which prognostic and predictive factors have been most extensively studied. Primary medical treatment (pre-surgical medical therapy) offers a unique setting to explore predictive factors due to the fact that primary breast cancers are easily accessible to repeated tissue sampling and evaluation of therapy response both clinically and radiologically. For many years, the investigators have studied predictive factors in primary medical treatment of breast cancer. In the present project, the investigators will implement a new trial concept where the current knowledge from previous trials with respect to predictive markers (hormone receptors, HER2; TP53, CHEK2 and RB1), will be combined with massive parallel sequencing (MPS). Thereby, the investigators aim to design the "next-generation" primary medical treatment where 1) therapy regimens are individualized based on a limited number of known predictive factors and, 2) MPS is used to explore additional predictive factors and their co-regulators in order to fully identify the mechanisms of drug sensitivity / resistance across individual tumours and pave the way for further personalized breast cancer therapy in the future. As for the new era of "genomic medicine", the current trial concept will allow individual tumours to be characterized by their unique gene mutation / epigenetic modification profile upfront, to allocate patients to their optimal personalized medicine as compared to "classical" drug testing through phase II/III trials.