Author ORCID Identifier

https://orcid.org/0000-0002-7413-1885

Date of Graduation

12-2020

Document Type

Dissertation (PhD)

Program Affiliation

Biomedical Sciences

Degree Name

Doctor of Philosophy (PhD)

Advisor/Committee Chair

G. Traver Hart, Ph.D.

Committee Member

Rehan Akbani, Ph.D.

Committee Member

Boyi Gan, Ph.D.

Committee Member

Anil Korkut, Ph.D.

Committee Member

John N. Weinstein, Ph.D., M.D.

Abstract

Innovation of CRISPR gene-editing technology has provided scientists genome manipulation tools that allowed rapid advancement of scientific capabilities and thus improved our ability to systematically study mammalian genetic functional profiles. Genome-wide CRISPR knockout screens conducted in collections of human cell lines can knock out genes at multiple loci, and have provided new insights into functional roles for independent genes. This method has launched massive efforts in looking across genetic backgrounds for context specific genetic vulnerabilities within cancer. Much of the research effort thus far has been spent on optimizing phenotype distinctions between essential, genes required for cell fitness, and non-essential, genes exhibiting no effect on cell fitness, gene sets.

Currently, there has been sparse investigation of proliferation suppressor genes, gene knockouts resulting in increased cellular growth rates, in the vast collection of genome-wide genetic screens coming from the Sanger and Broad Institute’s DepMap database. In this dissertation, we conducted a systematic survey of data coming from these respective databases to study proliferation suppressor gene measurements of genetic fitness. Our working hypothesis is that we can leverage the distinct fitness score signature of proliferation suppressor genes in a screen to identify pathway level proliferation suppressor biology and novel proliferation suppressor functions for specific genes. Analyses from these projects indicate that we have identified distinct patterns of genetic growth fitness distributions that are predicated on a tumor suppressor’s functional status. Additionally, we are able to leverage the fitness signature and detect other consistent genetic proliferation suppressors of cancer cells.

We have developed methodologies that can detect proliferation suppressor behavior within whole genome genetic fitness screens that is confirmed through other genomic metrics, specifically mutation and expression profiles. We additionally implemented network methodologies that demonstrate conserved pathways of proliferation suppression, as well as metabolic adaptation mechanisms. Finally, we have identified a short list of genes that have not been noted previously to have proliferation suppressor behavior, representing candidate tumor suppressor genes. Included in these novel hits are fatty acid synthesis genes demonstrating proliferation suppressor scores within a select group of acute myeloid leukemia cell lines.

Keywords

Tumor Suppressors, Genetic Fitness, CRISPR, Bioinformatics, Genomics, Cancer, Acute Myeloid Leukemia, Metabolism

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