Identifying Molecular Markers of Progression to Muscle Invasive Bladder Cancer
Standing
Undergraduate
Type of Proposal
Oral Presentation
Faculty
Faculty of Science
Proposal
An estimated 9,000 Canadians are diagnosed with bladder cancer each year making it the 5thmost common cancer in Canada, the 12thmost common among women and the 4thamong men. Most patients are initially diagnosed with non-muscle invasive bladder cancer (NMIBC), which in some cases can progress to muscle invasive bladder cancer (MIBC). MIBC is associated with significantly poorer prognosis than NMIBC, and it is unclear why some progress to MIBC while others do not. Thus, a better understanding of the molecular progression of bladder cancer is needed. Through a collaboration with the computer sciences department, our team has applied a machine learning method that identifies copy number variations associated with muscle invasion and identified three target genes, TP53, MLL2 and DDR2, which are able to predict MIBC with 91% accuracy. This project aims to investigate the validity of these findings. A panel of bladder cancer cell lines ranging from low to high grade will be utilized. Expression of TP53, DDR2 and MLL2 will be examined across the panel of cell lines and correlated with proliferative and invasive potential. Manipulation of the genes through either overexpression or knockdown experiments will allow us to determine if altering expression of these genes contributes to the progression of NMIBC to MIBC. This work seeks to identify molecular markers which predict progression to MIBC thus identifying novel prognostic and therapeutic targets.
Location
University of Windsor
Grand Challenges
Viable, Healthy and Safe Communities
Identifying Molecular Markers of Progression to Muscle Invasive Bladder Cancer
University of Windsor
An estimated 9,000 Canadians are diagnosed with bladder cancer each year making it the 5thmost common cancer in Canada, the 12thmost common among women and the 4thamong men. Most patients are initially diagnosed with non-muscle invasive bladder cancer (NMIBC), which in some cases can progress to muscle invasive bladder cancer (MIBC). MIBC is associated with significantly poorer prognosis than NMIBC, and it is unclear why some progress to MIBC while others do not. Thus, a better understanding of the molecular progression of bladder cancer is needed. Through a collaboration with the computer sciences department, our team has applied a machine learning method that identifies copy number variations associated with muscle invasion and identified three target genes, TP53, MLL2 and DDR2, which are able to predict MIBC with 91% accuracy. This project aims to investigate the validity of these findings. A panel of bladder cancer cell lines ranging from low to high grade will be utilized. Expression of TP53, DDR2 and MLL2 will be examined across the panel of cell lines and correlated with proliferative and invasive potential. Manipulation of the genes through either overexpression or knockdown experiments will allow us to determine if altering expression of these genes contributes to the progression of NMIBC to MIBC. This work seeks to identify molecular markers which predict progression to MIBC thus identifying novel prognostic and therapeutic targets.