DOI
https://doi.org/10.25772/8WG7-C354
Author ORCID Identifier
0000-0003-4956-7991
Defense Date
2022
Document Type
Thesis
Degree Name
Master of Science
Department
Human Genetics
First Advisor
Joseph Landry, Ph.D.
Abstract
Epigenetic modifications feature prominently in the biological changes necessary to promote tumor growth, invasion, and metastases. Lessons learned from clinical trials of first-generation epigenetic treatments for cancer have demonstrated a need for biomarker-driven clinical strategies, more highly specific drug targets to limit toxicity in humans, and the ability to use multiple drug targets in combination to increase efficacy of current cancer treatments in solid tumors. Future investigation of epigenetic targets would need to incorporate these lessons to ensure responsible research. However, attempting to address these needs by serialized laboratory experiments alone would be time and resource intensive. We demonstrate data science techniques that can be used to streamline the search for potential epigenetic targets for cancer therapies that should be prioritized for molecular follow-up. First, we use robust, publicly available databases to gain insight on Nucleosome Remodeling Factor (NURF), a chromatin remodeling complex that has shown to contribute to cancer cell biology in model organisms. In particular, we investigate the efficacy of inhibiting BPTF, a unique and essential subunit of NURF that has been reported to be a highly druggable potential target of the NURF complex. Then, we use internal RNA-sequencing data to gain insight about the anti-tumor mechanism of combination Guadecitabine and BPTF inhibition therapy. Finally, we use a CRISPR knock out (CRISPRKO) screen of 450 epigenetic regulators to identify novel epigenetic targets to prevent proliferative recovery of tumor cells. Study findings identify future directions for molecular studies for novel epigenetic targets in cancer therapy, including BPTF.
Rights
© The Author
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
5-10-2022
Included in
Bioinformatics Commons, Computational Biology Commons, Genetic Processes Commons, Genetics Commons, Genomics Commons, Oncology Commons