Many cancers have long been known to display an absolute requirement for the amino acid methionine (L-Met). Studies have shown that in the absence of L-Met, sensitive neoplasms experience cell cycle arrest and perish. Without the metabolic deviations that characterize L-Met auxotrophs, normal cells are able to grow on precursors such as homocysteine and tolerate periods of L-Met starvation. The differential requirement for this amino acid between normal and tumor cells has been exploited through enzymatic serum degradation of L-Met by a bacterial methionine-γ-lyase (MGL). Though MGL was able to deplete L-Met to therapeutically useful levels in animal models and exert a significant cytotoxic effect on malignant cell lines in vitro and on tumor xenografts in vivo, the clinical implementation of this enzyme is hampered by its short serum half-life and potential for catastrophic immune response. In the chapters that follow, we describe the engineering of a novel human methionine degrading enzyme (hMGL) that overcomes the limitations of the bacterial therapeutic. We have shown that hMGL is capable of degrading methionine at a therapeutically useful rate and inducing extensive cell killing in a variety of neoplasms. This enzyme is expected to have low immunogenicity in patients and a high therapeutic index. We have developed a high throughput screen for methionine degrading activity that we can utilize to further engineer the enzyme based on the results of additional preclinical development. We have found that hMGL is also capable of degrading cystine to operate as a dual amino acid depletion treatment that is expected to be more potent than methionine depletion alone. Due to the wide array of neoplasms sensitive to methionine and cystine starvation, the engineered enzyme holds a great deal of promise as a unique and powerful cancer therapeutic. / text
Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/31302 |
Date | 10 September 2015 |
Creators | Paley, Olga M. |
Contributors | Georgiou, George |
Source Sets | University of Texas |
Language | English |
Detected Language | English |
Type | Thesis |
Format | application/pdf |
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