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Statistical Methods for Molecular Genetic Analysis of Breast Cancer

Project Type: 
PDF-led

The objective of the project is to develop, evaluate and apply informative statistical methods to the task of identifying novel genes/pathways involved in breast cancer recurrence.

Project Leader(s): 

Postdoctoral Fellow: Dr. Yildiz Yilmaz, Dalla Lana School of Public Health, University of Toronto

Lead faculty member: Dr. Shelley Bull, Dalla Lana School of Public Health, University of Toronto

The objective of the project is to develop, evaluate and apply informative statistical methods to the task of identifying novel genes/pathways involved in breast cancer recurrence. A model for time to cancer recurrence using clinical, pathological, and molecular measures in the setting of high-dimensional genome-wide genetic scans will be developed that allows for a proportion of the patients to be long-term survivors. These so-called cure models will be used to address issues in design and analysis of on-going molecular genetic research that aims to investigate clinical, pathological and molecular measures in breast cancer, assess molecular genetic information as prognostic factors for survival, and develop risk prediction models. Providing biomedical researchers with access to new more powerful analytic tools will facilitate the discovery of potential candidates for the development of new therapeutic interventions and determine direction for further studies of underlying fundamental biological mechanisms in Samuel Lunenfeld Research Institute.