This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Description
Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide and exhibits a male-bias in occurrence and mortality. Previous studies have provided insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and mortality. This study uses pathway analysis to add insight

Hepatocellular carcinoma (HCC) is the third leading cause of cancer death worldwide and exhibits a male-bias in occurrence and mortality. Previous studies have provided insight into the role of inherited genetic regulation of transcription in modulating sex-differences in HCC etiology and mortality. This study uses pathway analysis to add insight into the biological processes that drive sex-differences in HCC etiology as well as a provide additional framework for future studies on sex-biased cancers. Gene expression data from normal, tumor adjacent, and HCC liver tissue were used to calculate pathway scores using a tool called PathOlogist that not only takes into consideration the molecules in a biological pathway, but also the interaction type and directionality of the signaling pathways. Analysis of the pathway scores uncovered etiologically relevant pathways differentiating male and female HCC. In normal and tumor adjacent liver tissue, males showed higher activity of pathways related to translation factors and signaling. Females did not show higher activity of any pathways compared to males in normal and tumor adjacent liver tissue. Work suggest biologic processes that underlie sex-biases in HCC occurrence and mortality. Both males and females differed in the activation of pathways related apoptosis, cell cycle, signaling, and metabolism in HCC. These results identify clinically relevant pathways for future research and therapeutic targeting.
ContributorsRehling, Thomas E (Author) / Buetow, Kenneth (Thesis advisor) / Wilson, Melissa (Committee member) / Maley, Carlo (Committee member) / Arizona State University (Publisher)
Created2021
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Description
The representation of a patient’s characteristics as the parameters of a model is a key component in many studies of personalized medicine, where the underlying mathematical models are used to describe, explain, and forecast the course of treatment. In this context, clinical observations form the bridge between the mathematical frameworks

The representation of a patient’s characteristics as the parameters of a model is a key component in many studies of personalized medicine, where the underlying mathematical models are used to describe, explain, and forecast the course of treatment. In this context, clinical observations form the bridge between the mathematical frameworks and applications. However, the formulation and theoretical studies of the models and the clinical studies are often not completely compatible, which is one of the main obstacles in the application of mathematical models in practice. The goal of my study is to extend a mathematical framework to model prostate cancer based mainly on the concept of cell-quota within an evolutionary framework and to study the relevant aspects for the model to gain useful insights in practice. Specifically, the first aim is to construct a mathematical model that can explain and predict the observed clinical data under various treatment combinations. The second aim is to find a fundamental model structure that can capture the dynamics of cancer progression within a realistic set of data. Finally, relevant clinical aspects such as how the patient's parameters change over the course of treatment and how to incorporate treatment optimization within a framework of uncertainty quantification, will be examined to construct a useful framework in practice.
ContributorsPhan, Tin (Author) / Kuang, Yang (Thesis advisor) / Kostelich, Eric J (Committee member) / Crook, Sharon (Committee member) / Maley, Carlo (Committee member) / Bryce, Alan (Committee member) / Arizona State University (Publisher)
Created2021