Prostate cancer (PC) is the third leading cause of cancer death amongst men in the developed world. Most aging men will develop PC, yet the life-time risk of a PC-caused death is only 3%. For this reason, care givers aim to distinguish between clinically significant and insignificant disease. However, risk evaluation remains a challenge due in part to PC’s both inter- and intra-tumour heterogeneity. Despite recent findings that highlight the wide range of tumour variability, standard PC diagnostics does not include genomic profiling resulting in a lack of personalized therapies for patients with castration-resistant PC.
The PrECISE consortium’s partners share the aim of developing algorithms and technologies to improve risk evaluation of PC patients. This improvement is much needed to avoid unnecessary treatments that heavily deteriorate the patient’s quality of life, reduce the financial burden associated with over-treatment, and focus available treatment capacity on those patients that actually benefit from it. Moreover, few treatment options are available for aggressive drug-resistant PCs. Diagnosis and treatment of these tumours will benefit from identification of predictive biomarkers and the master regulators of drug-resistance, but these markers can only be found if appropriate techniques that take into account tumour heterogeneity are developed. The consortium has tackled the problem by profiling DNA from multiple biopsies per tumour and by integrating different type of omics data in order to characterise the molecular state of each patient at different malignancy stages. Furthermore, PrECISE also aims to develop new approaches to suggest chemotherapy drugs and targeted therapies for each patient. We have investigated molecular mechanisms and identified suitable intervention points for therapy that helps designing personalized therapies based on each patient’s molecular signatures. Finally, we want to make our results easily accessible to scientists, clinicians and patients. With this aim in mind, we focused our efforts in developing PrECISE algorithms as open-source software or open access web services, mostly available on a common platform known as the SmartBiobank.
