Translational Research

Translational Research

Translational research transforms scientific discoveries arising from fundamental studies into clinical applications to improve outcomes for patients with prostate cancer. Members of this team work together to employ innovative new technologies for biomarker discovery, detection and quantification that have been developed independently in internationally-recognized efforts in Alberta and bring them to bear on a common goal – developing better tests for prostate cancer.

The combination of prognostic biomarkers from distinct aspects of prostate cancer progression resulted in a test that is more sensitive, more accurate and more cost effective than current tests for prostate cancer.

Our overall goal is to improve patient outcomes and quality of life by translating novel tests to the clinic that are more sensitive, more accurate and more cost-effective than current tests for prostate cancer. No one dies from prostate cancer that is localized to the prostate – the spread, or metastasis, of prostate cancer is what makes it so dangerous. The five year survival rate for localized prostate cancer in North America is close to 100%, yet this drops to 30% for metastatic disease. The metastasis of prostate cancer is driven primarily by changes in growth factors, extracellular proteases, and the cell migration machinery. Biomarkers that are involved in these processes are prognostic for metastatic prostate cancer and their integration into a single powerful test are the focus of this multi-disciplinary team.

Our team is integrating internationally-recognized efforts in the characterization of tumour cell migration, growth factor signaling and protease-activated receptors to develop novel multiplex tests to more accurately predict outcomes in prostate cancer.

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Russ Greiner’s Team “PC LEARN”, tied for 1st in the Prostate Cancer DREAM Challenge

Competing with 50 teams from around the world in the Prostate Cancer DREAM Challenge, University of Alberta’s PC LEARN team tied for 1st in one of the 3 sub-challenges
to predict the survival and toxicity of Docetaxel treatment in patients with metastatic castrate resistant prostate cancer!

“The DREAM Challenge was an exciting opportunity for us to apply machine learning to real medical data and possibly to contribute to medical research.” said lead PI and APCaRI member Russ Greiner.

The primary benefit of this Challenge will be to establish new quantitative benchmarks for prognostic modeling in mCRPC, with a potential impact for clinical decision making and ultimately understanding the mechanism of disease progression. https://www.synapse.org/#!Synapse:syn2813558/wiki/70844

- Russ Greiner