A Spatial Multi-Omics Approach to Unravel Prostate Tumour Heterogeneity

Prostate cancer (PCa) is the most commonly diagnosed cancer in men in the developed world. Particularly stable disease control is challenging. Still in 2012, an estimated 1.1 million men worldwide were diagnosed with PCa and 307 000 men died of this disease. As prostate epithelial cells are exquisitely dependent on male sex hormones (androgens), the primary treatment for advanced PCa is suppression of androgen production, usually combined with antagonists that inhibit the androgen receptor (AR), the mediator of androgen action. Whilst these treatments are initially effective, nearly all patients eventually relapse. In fact, we are convinced that prostate tumour heterogeneity, which is a readout of the lipid composition of PCa cells, is the key underlying issues for this phenomenon. In addition, lipid metabolism is dramatically altered in PCa, is strongly affected by (anti)-androgens and is tightly linked with cancer biology. There is an urgent need for more reliable clinical indicators indicating the best fitting therapy. This research project tries to unravel prostate tumour heterogeneity using state-of-the-art mass spectrometry imaging approaches (MALDI-MSI) on clinical samples. Based on these results we want to develop digital pathological approaches for diagnostics of PCa. These digital pathological approaches will indicate the best fitting personalised therapy entirely based on the lipid profiles of the patient's prostate tumour.