Title page for ETD etd-05092011-161650


Type of Document Master's Thesis
Author Song, Lei
Author's Email Address polosong@vt.edu
URN etd-05092011-161650
Title Computational Analysis of Genome-Wide DNA Copy Number Changes
Degree Master of Science
Department Electrical and Computer Engineering
Advisory Committee
Advisor Name Title
Wang, Yue J. Committee Chair
Lu, Chang-Tien Committee Member
Xuan, Jianhua Committee Member
Keywords
  • DNA Copy Number Changes
  • Circular Binary Segmentation
  • Haar Wavelet Transform
  • Chromosome Instability Index
  • Georgetown Database of Cancer
Date of Defense 2011-05-03
Availability unrestricted
Abstract
DNA copy number change is an important form of structural variation in human genome. Somatic copy number alterations (CNAs) can cause over expression of oncogenes and loss of tumor suppressor genes in tumorigenesis. Recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale, with high resolution. Quantitative analysis of somatic CNAs on genes has found broad applications in cancer research. Most tumors exhibit genomic instability at chromosome scale as a result of dynamically accumulated genomic mutations during the course of tumor progression. Such higher level cancer genomic characteristics cannot be effectively captured by the analysis of individual genes. We introduced two definitions of chromosome instability (CIN) index to mathematically and quantitatively characterize genome-wide genomic instability. The proposed CIN indices are derived from detected CNAs using circular binary segmentation and wavelet transform, which calculates a score based on both the amplitude and frequency of the copy number changes. We generated CIN indices on ovarian cancer subtypes‘ copy number data and used them as features to train a SVM classifier. The experimental results show promising and high classification accuracy estimated through cross-validations. Additional survival analysis is constructed on the extracted CIN scores from TCGA ovarian cancer dataset and showed considerable correlation between CIN scores and various events and severity in ovarian cancer development.

Currently our methods have been integrated into G-DOC. We expect these newly defined CINs to be predictors in tumors subtype diagnosis and to be a useful tool in cancer research.

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