Access Type

Open Access Dissertation

Date of Award

January 2016

Degree Type

Dissertation

Degree Name

Ph.D.

Department

Physiology

First Advisor

Ghassan M. Saed

Abstract

Epithelial ovarian cancer is the deadliest of all gynecologic cancers with an estimated 22,280 new cases and 14,240 deaths expected in 2016 in the US alone. This high mortality rate can be partially attributed to a lack of universal screening and the development of resistance to the recommended chemotherapeutics. Typically, the treatment of ovarian cancer requires both cytoreductive surgery (CRS) and platinum/taxane combination chemotherapy. Initially, 50–80% of patients with advanced disease will achieve complete clinical response. Unfortunately, most will relapse within 18 months with chemoresistant disease. Thus, understanding the mechanisms of platinum resistance is critical in order to improve the care of ovarian cancer patients. Several theories to explain cisplatin resistance have been proposed but failed to translate into clinical practice. Typically drug resistance mechanisms are multifactorial but can be broadly categorized as follows: 1) pharmacokinetic, resulting in inadequate intratumor cisplatin concentration, 2) tumor micro-environment, involving membrane transporters by reducing cisplatin uptake or increasing efflux, 3) increased inactivation and sequestration of cisplatin, 4) activation of DNA repair and antiapoptotic mechanisms, 5) decreased autophagy and, 6) cancer-cell specific mechanisms such as: acquired somatic mutations and epigenetic changes and persistence of slow growing cancer stem cells that maintain the cancer phenotype.

We have recently characterized EOC tissues and cells to manifest a persistent pro-oxidant state with the upregulation of several key oxidant enzymes including: myeloperoxidase (MPO), inducible nitric oxide synthase (iNOS), and nicotinamide adenine dinucleotide phosphate (NAD(P)H) oxidase with concurrent decrease in apoptosis compared to normal human ovarian tissues and cells. More importantly, shutting down the expression of one or more of these key oxidant enzymes reduced the pro-oxidant state, and significantly induced apoptosis. Single-nucleotide polymorphisms (SNPs) are point mutations that are selectively maintained in populations and are distributed throughout the human genome at an estimated overall frequency of at least one in every 1000 base pairs. Several SNPs in key oxidants and antioxidants enzymes have been associated with various cancers including ovarian. Therefore, we are proposing the following hypothesis: cisplatin treatment induces mutations in key oxidant and antioxidant enzymes resulting in further enhancement of oxidative stress and the acquisition of resistance in EOC cells. To test this hypothesis, we are proposing then following specific aims:

Specific Aim 1: To determine the association of specific single nucleotide polymorphisms (SNPs) in key oxidant and antioxidant enzymes with EOC risk and survival in patients.

The rationale of this aim is based on the fact that oxidative stress is strongly associated with several cancers, including ovarian. Known specific SNPs in oxidant and antioxidant genes may alter their expression profile and enzymatic functions. These SNPs have been reported to be associated not only with cancer risks, but also patient response to treatment and survival. The hypothesis of this aim is that specific SNPs in key oxidant and antioxidant enzymes are associated with overall patient survival. To achieve this aim, we will perform a case-control study using stored blood samples of research participants from the Karmanos Cancer Center. Individuals (n=143) recruited were divided into controls (n=94),) and ovarian cancer cases (n=49). Samples will undergo DNA extraction followed by TaqMan® SNP genotype analysis for rs4880 manganese superoxide dismutase (MnSOD), rs4673 (NAD(P)H oxidase (CYBA), rs3448 glutathione peroxidase (GPX1), rs2297518 inducible nitric oxide synthase (iNOS), rs1002149 glutathione reductase (GSR), and rs1001179 catalase (CAT). We will perform a multivariate analysis for identification of confounding variables and potential predictors of risk. Additionally, to study the impact of the SNPs on overall survival, Cox regression and Kaplan-Meier survival analyses will be used.

Specific Aim 2: To determine the association of key oxidant and antioxidant enzymes as well as specific SNPs in these enzymes with the development of cisplatin resistance in EOC cells.

The rationale of this aim is based on previous findings showing an association between the altered redox enzymes and EOC, in both patients and human cell lines. The hypothesis of this aim is that the acquisition of resistance to cisplatin in EOC cells is associated with enhanced pro-oxidant profile, as well as specific SNPs in key oxidant and antioxidant enzymes. To achieve this aim, we will utilize two human EOC cell lines, MDAH-2774 and SKOV-3 and their cisplatin resistant counterparts. We will perform TaqMan PCR genotyping, real-time RT-PCR, ELISA, and Griess assay to study the expression profile of the following genes: CYBA/NOX4, iNOS, CAT, SOD3, GSR and GPX1. To analyze the difference in the expression profiles of these genes for sensitive compared to resistant cells, we will use a Student’s t-test.

Specific Aim 3: To determine whether specific SNP(s) in key oxidant and antioxidant enzymes cause the acquisition of cisplatin resistance in EOC cells.

The rationale of this aim is based on the established fact that cisplatin treatment causes DNA damage, and the observation that specific SNPs in the redox enzymes were found to be associated with poor survival in patients. The hypothesis of this aim is: specific SNPs in key oxidant and antioxidant enzymes cause cisplatin resistance. To achieve this aim, we will utilize the CRISPR/Cas9 system to generate point mutations in sensitive EOC cells corresponding to the SNP genotype of the chemoresistant MDAH-2774 and SKOV-3 EOC cells. The cells will then be tested for cisplatin resistance using the MTT viability assay using the IC50 method. Results will be analyzed with regression analysis and student t-tests.

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