JSM 2011 Online Program

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Abstract Details

Activity Number: 301
Type: Contributed
Date/Time: Tuesday, August 2, 2011 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #301511
Title: Bioinformatics of High-Throughput Insertional Mutagenesis to Identify Cancer-Causing Genes
Author(s): Jean A. Roayaei*+ and Keiko Akagi
Companies: National Cancer Institute and The Ohio State University
Address: 18 Miller Drive, Frederick, MD, 20712,
Keywords: CIS ; Founders Mutation ; Breast cancer-subtypes ; Poisson Distribution ; MCMC Gibbs Sampling ; Human Genome
Abstract:

We investigated Common Insertional Sites (CISs) for human cancer genome to identify cancer causing genes in breast cancer. We considered different window sizes of 1KB, 2KB, 5KB and 10KB to identify genes that have been implicated in different breast cancer sub-types. Cancer gene mutations are rare events whether they are founder's mutations (the first incidence of breast cancer in a family) or in women who have genetic suseptibility to breast cancer. We used a Poisson probability distribution to simulate the number of cancer causing genes by comparing a random set versus a test set of different window sizes for Common Insertional Sites. We identified cancer causing genes for different breast cancer sub-types that were not implicated in previous NCI breast cancer studies. This could be used by clinicians to develop new treatments for different patients' cohorts with different breast cancer subtypes.


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