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Modeling Brain Tumors: Simulating individual Patient Cases of Glioblastoma Multiforme

Description
Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median…

Glioblastoma multiforme (GBMs) is the most prevalent brain tumor type and causes approximately 40% of all non-metastic primary tumors in adult patients [1]. GBMs are malignant, grade-4 brain tumors, the most aggressive classication as established by the World Health Organization and are marked by their low survival rate; the median survival time is only twelve months from initial diagnosis: Patients who live more than three years are considered long-term survivors [2]. GBMs are highly invasive and their diffusive growth pattern makes it impossible to remove the tumors by surgery alone [3]. The purpose of this paper is to use individual patient data to parameterize a model of GBMs that allows for data on tumor growth and development to be captured on a clinically relevant time scale. Such an endeavor is the rst step to a clinically applicable predictions of GBMs. Previous research has yielded models that adequately represent the development of GBMs, but they have not attempted to follow specic patient cases through the entire tumor process. Using the model utilized by Kostelich et al. [4], I will attempt to redress this deciency. In doing so, I will improve upon a family of models that can be used to approximate the time of development and/or structure evolution in GBMs. The eventual goal is to incorporate Magnetic Resonance Imaging (MRI) data into a parameterized model of GBMs in such a way that it can be used clinically to predict tumor growth and behavior. Furthermore, I hope to come to a denitive conclusion as to the accuracy of the Koteslich et al. model throughout the development of GBMs tumors.
ContributorsManning, Miles (Author) / Kostelich, Eric (Thesis director) / Kuang, Yang (Committee member) / Preul, Mark (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12
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Can Akt3 Decrease Tumorigenicity in Glioblastoma Multiforme Through a Cell Cycle Mechanism?

Description
Glioblastoma multiforme is associated with a very low survival rate and is recognized as the most vicious form of intracranial cancer. The Akt gene pathway has three different isoforms, each of which has a different role in the tumors of GBM. Preliminary data suggests that Akt3 may work to decrease…

Glioblastoma multiforme is associated with a very low survival rate and is recognized as the most vicious form of intracranial cancer. The Akt gene pathway has three different isoforms, each of which has a different role in the tumors of GBM. Preliminary data suggests that Akt3 may work to decrease tumorigenicity. A produced image that visualizes the subcellular localization of Akt3 led the author to believe that Akt3 may reduce tumorigenicity by decreasing genomic instability caused by the cancer. To explore this, flow cytometry was performed on GBM cell lines with Akt3v1 over-expression, Akt3v2 over-expression, and a control glioma cell line.
ContributorsGhorayeb, Antoine (Author) / Neisewander, Janet (Thesis director) / Diehnelt, Chris (Committee member) / Moussallem, Suzan (Committee member) / Barrett, The Honors College (Contributor) / College of Liberal Arts and Sciences (Contributor)
Created2012-12