In-silico identification of driver alterations and prediction of therapeutics targeting actionable drivers in prostate cancer

Cancer arises as a result of deleterious aberrations in the DNA, RNA and proteins of cells. The accumulation of genetic abnormalities, over lifetime, perturbs critical functions of cells, which may ultimately give rise to tumor. However, vast majority of these aberrations are not essential and only small fractions, known as driver genes, are critical for tumor growth. Identification of driver aberrations is a challenging task, but is critical for optimal cancer management. Prostate cancer (PCa) is the most common cancer to affect Canadian men. Our laboratory has access to the DNA and RNA sequences of PCa patients. Using advanced mathematics and computer science techniques, we propose to establish linkage between driver genes and tumour viability and thus reveal novel biological insights to therapeutic strategies. The identified driver genes will represent new candidates for actionable therapeutic targets. This will guide the selection of appropriate drugs and development of new ones. Thus this work will further aid in the development of new strategies in precision cancer medicine for PCa.

Faculty Supervisor:

Colin Collins

Student:

Raunak Shrestha

Partner:

iProgen

Discipline:

Medicine

Sector:

Pharmaceuticals

University:

University of British Columbia