Jaime Ramirez-Vick, Ph.D. (Advisor); Nasim Nosoudi, Ph.D. (Committee Member); Amir Zadeh, Ph.D. (Committee Member)
Master of Science in Biomedical Engineering (MSBME)
Triple-negative breast cancer (TNBC) is characterized by the absence of expression of the estrogen receptor, progesterone receptor and human epidermal growth factor receptor 2 (HER2). Therefore, TNBC is unresponsive to targeted hormonal therapies, which limits treatment options to nonselective chemotherapeutic agents. Basal-like breast cancers (BLBCs) represent a subset of about 70% of TNBCs, more frequently affecting younger patients, being more prevalent in African-American women and significantly more aggressive than tumors of other molecular subtypes, with high rates of proliferation and extremely poor clinical outcomes. Proper classification of BLBCs using current pathological tools has been a major challenge. Although TNBCs have many BLBC characteristics, the relationship between clinically defined TNBC and the gene expression profile of BLBC is not fully examined. The purpose of this study is to assemble publicly-available TNBC gene expression datasets generated by Affymetrix gene chips and define a set of genes, or gene signature, that can classify TNBC samples between BLBC and Non-BLBC subtypes. We compiled over 3,500 breast cancer gene expression profiles from several individual publicly available datasets and extracted Affymetrix gene expression data for 580 TNBC cases. Several popular data mining methods along with dimensionality reduction and feature selection techniques were applied to the resultant dataset to build predictive models to understand molecular characteristics and mechanisms associated with BLBCs and to classify them more accurately according to important features extracted through microarray data analysis of BLBC and Non-BLBC cases. Our result can lead to proper identification and diagnosis of BLBCs, which can potentially direct clinical implications by dictating the most effective therapy.
Year Degree Awarded
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