Preventive examination for Prostate cancer (PCa) via prostate specific antigen, needle biopsy and histopathological examination has increased the detected number of localized PCa and provided a first model for risk stratification. However, this model has an imminent need to be rectified in order to a) stratify patient’s treatment (surveillance vs. surgery vs. radiation therapy), b) identify patients with high-risk tumors and c) improve disease progression monitoring. The presented consortium concatenates routine clinical course with state of the art omics and bioinformatics analyses derived at primary diagnosis; employing a) deep proteomics of tissue specimens, b) Next Generation Sequencing of seminal vesicle plasma and matched tissue; and c) imaging techniques (PSMA PET, and MRI) as a radiomics. These high dimensional data will be integrated into statistical learning models. The deep and potentially unbiased feature extraction will pave the way for personalized therapy predicting models and risk-profiles; with a strong emphasis on non-invasive approaches.
