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Title: Characterization of a Gene Signature Predictive of Response and Prognosis with Conventional and DNA Vaccine-Based EGFR Family Targeted Therapy in Ovarian Cancer
Authors: Hernandez, Patricia
Advisors: Notterman, Daniel
Department: Molecular Biology
Certificate Program: Global Health and Health Policy Program
Class Year: 2018
Abstract: Overexpression of epithelial growth factor receptor (EGFR) and human epidermal growth factor receptor 2 (HER2) are associated with poor prognosis and decreased therapeutic responsiveness in ovarian cancer. Previous studies evaluating conventional anti-EGFR and anti-HER2 drugs have demonstrated minimal success in inhibiting ovarian cancer progression. As it stands, there are no established methods to distinguish between patients who will respond to inhibitors of the EGFR family and those who will not. To this end, we sought to examine a novel strategy for an active form of transient immunization using synthetic plasmid cassettes expressing an anti-HER2 functional monoclonal antibody (MAb). Upon intramuscular administration, lasting levels of anti-HER2 antibodies were observed in mice. The MAbs generated displayed cellular cytotoxicity activity against tumor cells. Additionally, we used microarray analysis of ovarian cancer cell lines to identify a gene expression signature that can be used to predict to whom EGF-targeted therapy should be offered. A novel 18-gene signature predictive of EGFR inhibition response was identified using clustering, gene set enrichment, principal component analysis, and machine learning methods. We demonstrated the gene signature’s generalizability as a classifier of resistant phenotypes by examining genetic profiles of ovarian cancer cell lines treated with other receptor tyrosine kinase inhibitors. Our gene signature also revealed potential candidate target genes for future treatment efforts in ovarian cancer and highlighted the invasive and mesenchymal nature of resistant ovarian cancer cells. Moreover, the novel 18-gene signature was an independent predictor of patient progression-free survival [hazard ratio (HR): 1.39, 95% CI, 1.23-1.58, p = 0.00000027] and may serve as a prognostic tool in ovarian cancer. Together, these findings provide a basis for improving the current prognosis of women diagnosed with ovarian cancer.
Type of Material: Princeton University Senior Theses
Language: en
Appears in Collections:Molecular Biology, 1954-2023
Global Health and Health Policy Program, 2017-2023

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