Background: Prostate cancer (PC) is a common and variable disease. It is difficult to predict tumor aggressiveness at diagnosis and new markers for prognostication and therapy response are urgently needed. Mechanisms behind PC progression need to be discovered in order to expose new therapies for currently incurable metastatic disease.
Specific aims: 1) to apply proteomic, metabolomic, and genomic techniques to biobank samples and animal models in order to discover biomarkers in tissue and blood, which are related to disease aggressiveness, response to therapy and clinical out-come and 2) to use those markers to identify putative therapeutic targets.
Access to biobanks of tumor tissue and blood, covering all stages of the disease, in combination with relevant experimental models, state-of-the-art equipments, and collaborative work with clinicians and multivariate statisticians create a unique scientific environment. We perform proteomic- (2D DIGE, SELDI-TOF MS, tissue-array), metabolomic- (GC-MS, LC-MS), and genomic- (expression array) based studies of tumor tissue and plasma in case-control series and experimental models. Prognostic models are built from training data sets and promising biomarkers are identified and validated in separate test sets including prospective samples. We specifically study bone metastases with the goal to find mechanisms behind metastatic growth. The biological roles of specific biomarkers are elucidated in vitro and in animal models.