Author ORCID Identifier

0000-0003-1442-1589

Date of Graduation

5-2022

Document Type

Dissertation (PhD)

Program Affiliation

Biostatistics, Bioinformatics and Systems Biology

Degree Name

Doctor of Philosophy (PhD)

Advisor/Committee Chair

Traver Hart, PhD

Committee Member

Ronald DePinho, MD, PhD

Committee Member

Anil Korkut, PhD

Committee Member

John Paul Shen, MD

Committee Member

Eduardo Vilar Sanchez, MD, PhD

Abstract

The advent of CRISPR technology and its adaptation to the mammalian genome made whole-genome knockout screens possible directly in human cells. Gene knockout answers how essential that gene is for cell fitness and proliferation. Genes showing moderate to severe fitness defects are called essential genes and provide insights into disease-specific candidate therapeutic targets. Additionally, CRISPR offers other applications for genome editing. Two applications this dissertation is based on are 1) combination of gene knockout and drug treatment, which enables the identification of chemogenetic interactions, or gene mutations that enhance or suppress the activity of a drug, and 2) combinatorial editing, which facilitates the examination of possible genetic interaction between the two perturbed genes. Both chemogenetic and genetic interactions have the potential to decode the mechanisms of cancer diseases and provide an avenue for new therapeutic strategies.

CRISPR-mediated chemogenetic screens have primarily been used in positive selections screens. Therefore, allowing only the identification of genetic modifications involved in resistance mechanisms. In the first part of this dissertation, I describe drugZ, an algorithm that addressed the need for identifying both, genetic modifications involved in synthetic lethality as well as in resistance mechanisms. In addition to identifying known and novel chemogenetic interactions, I show that drugZ also provides insights into the experimental design of pooled CRISPR screens. The second part of this dissertation is focused on predicting the synthetic lethal interactions, which are the most frequently investigated genetic interactions. Very few of these interactions have been reproduced across multiple studies and many appear highly context-specific. Thus, the major drawback is the lack of gold standards synthetic lethal interactions and a baseline probability of being synthetic lethal for any given gene pair, independent of the molecular background. I address this drawback by predicting the context-independent synthetic lethal probability with Bayes’ theorem, through the integration of existing CRISPR-based genetic interaction screens and other functional genomics data types.

Collectively, this work provides analytical methods that advance the field of functional genomics, a significant understanding of chemogenetic and genetic interactions in human cancer cells, insights about optimized, less time and effort-consuming experimental design, and an avenue for generating new therapeutic opportunities.

Keywords

Functional genomics, CRISPR technology, Bioinformatics, Genetic interactions, Synthetic lethality, Chemogenetic response

Available for download on Friday, September 02, 2022

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