Cancer is a leading cause of morbidity and mortality, and one in three individuals in the U.S. will be diagnosed with cancer in their lifetime. At the molecular level, cancer is driven by the activity of oncogenes and the loss of activity of tumor suppressors. The availability of genomic data from large sets of tumor tissue have facilitated the identification of subgroups of patients whose tumors share molecular patterns of expression. These molecular signatures, in turn, can help identify clinically-useful patient subgroups and inform potential therapeutic strategies against cancer.
In chapter 1, I review the current theories behind carcinogenesis, the molecular factors that regulate gene expression, and statistical methods for analyzing genomic data. In chapter 2, I describe an approach, termed oncomix, developed to identify oncogene candidates from expression data obtained from tumor and adjacent normal tissue. I apply oncomix to breast cancer expression data and identify an oncogene candidate, CBX2, whose expression is gained in a subset of breast tumors. CBX2 is expressed at low levels in most normal adult tissue, and the CBX2 protein contains a drug-targetable chromodomain, both of which are desirable properties in a potential therapeutic target. We then provide the first experimental evidence that CBX2 regulates the growth of breast cancer cells. In chapter 3, I develop a method for identifying nuclear hormone receptors whose expression is lost in endometrial cancers relative to normal tissue. I report, for the first time, that the loss of expression of Thyroid Hormone Receptor Beta (THRB) is associated with better 5-year survival in endometrial cancer. The loss of THRB expression is independent of the loss of estrogen and progesterone receptor expression, two genes whose loss of expression is known to be associated with poor survival. THRB expression could be considered as a biomarker to risk-stratify endometrial cancer patients. In Chapter 4, I develop a user-friendly application for visualizing chromosomal copy number state obtained from three types of copy number input in single cells – fluorescence in situ hybridization (FISH), spectral karyotyping (SKY), and whole genome sequencing (WGS). This web application, termed aneuvis, automatically creates novel visualizations and summary statistics from a set of user-uploaded files that contain chromosomal copy number information.
In this thesis, I develop new computational approaches for identifying candidate molecular regulators of cancer. I also develop a new user-friendly tool to enable biological researchers to identify aneuploidy and chromosomal instability within populations of single cells. Applying these tools to breast and endometrial cancer genomic datasets has highlighted novel aspects of breast and endometrial cancer biology and may inform novel therapeutic strategies based on molecular patterns of genomic heterogeneity. The freely available software developed as part of these projects has the potential to enable other researchers to advance our understanding of cancer genomics and to inform novel therapeutic strategies against cancer.
|Advisor:||Mar, Jessica, Greatly, John|
|School Location:||United States -- New York|
|Source:||DAI-B 80/03(E), Dissertation Abstracts International|
|Subjects:||Systematic biology, Bioinformatics, Oncology|
|Keywords:||Aneuploidy, Bioinformatics, Breast Cancer, Endometrial Cancer, Mixture Models, Transcriptomics|
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