Breast cancer is a heterogeneous disease that varies in forms, clinical outcomes, genomic and genetic alterations, and molecular classifications. Researchers have demonstrated an association between changes in DNA copy number values (i.e copy number alterations/CNAs) and the development and progression of cancer. Given the large number of CNAs that exist in human breast cancers, finding the most frequent and important CNAs is key to advancing therapeutics because it is likely that these recurrent CNAs are breast tumor drivers. We propose to identify these key drivers through a comparative genomics approach, which may simultaneously identify new drivers, and provide an animal model for further in vivo breast cancer treatment testing.
The long-term goal of this project is to develop a statistical and computational framework, including methods and software, for CNA analysis and related studies aimed at identifying recurrent CNAs, and the driving genes from these CNAs. We have two datasets containing human tumors and one containing mouse models. Of our two human datasets, one set contains patient tumors from the Perou Lab and another set from the Cancer Genome Atlas Project, TCGA. The volume of data generated is immense and the use of high performance computing (HPC) is vital to analyze data, quality control, and store the information for future use. By investing in HPC we have increased our power to exploit the growing amounts of patient data. The knowledge gained allows us to predict patient’s relapse-free survival, overall survival, responsiveness to drug treatment, and further develop better cancer therapies.
More about this speaker:
Grace Silva earned a B.S. in bioinformatics and computational biology from the University of Maryland, Baltimore County where as a Meyerhoff Scholar I was first introduced to biological and biomedical research opportunities. During my time as an undergraduate I attended the Research Experience for Undergraduates at Princeton University and the Stanford Summer Research Program where I developed an interest in cancer research. Currently, I am a 3rd year bioinformatics and computational biology graduate student at the University of North Carolina, at Chapel Hill. My thesis work involves profiling copy number alterations in breast cancer using human tumors and mouse models.