Morten Beck Rye
Background and activities
My research activity is in the translational field between computational transcriptomics, metabolomics, epigenetics and gene regulation to improve disease outcome predictions and treatment decisions for prostate cancer. This is achieved through a collaboration between BiGR and the MR Cancer Group at NTNU and Department of Urology at St. Olavs Hospital. In our research, I and my colleagues are trying to understand what separates aggressive from indolent prostate cancers at the molecular level through integration of data from various high throughput technologies.
Due to increased focus on men’s health through campaigns and public debate, it is expected that the number of men tested for prostate cancer will increase. A challenge in current prostate cancer diagnostics is the separation of prostate cancer in low, intermediate and high risk groups based on the acquired clinical data, which includes MR-images, tissue biopsies and blood-samples. Limitations in the classification system sometimes lead to undertreatment of high risk cancers. However, a more common problem is probably the tendency to overtreat low risk cancers resulting in unwanted side-effects from treatment for a large group of patients. The goal of our research is to improve the detection of high and low risk prostate cancer in clinical care, both at initial diagnosis and during active surveillance of the disease.
We are currently developing robust classifiers for aggressive prostate cancer based on molecular data from tissue biopsies. However, our long term vision is that aggressive prostate cancer should be classified through minimally invasive blood-samples or non-invasively through MR-imaging. To achieve this we need to focus on extensive analysis of patient tissue samples using state-of-the-art technology improve our understanding on how prostate tissue characteristics, molecular signals and image features are connected. This is currently the focus of the ongoing ProstOmics project.
Current research directions:
Development and validation of a gene expression signature which can robustly separate indolent and aggressive prostate cancer from clinical patient biopsies.
Inegrating data produced in the ProstOmics project with other huge resources of publicly available data to connect molecular data with tissue and image features. The long-term goal is to improve non-invasive classification of aggressive prostate cancer
Investigating the potential of epigenetic markers for detection of aggressive prostate cancer in blood, urine and prostatic fluid.
In addition I am also involved in the Bioinformatics Core Facility and the Bioinformatics Helpdesk for Elixir Norway, where I am assisting other researchers with bioinformatics analysis, particularly involving Next Generation Sequencing (NGS) data. I am also responsible for an annual course in NGS analysis, hosted biannually by NORBIS.