Date of Award

Spring 5-2023

Document Type


Degree Name

Doctor of Philosophy (PhD)


Pharmaceutical Sciences

First Advisor

Moom Roosan, Ph.D, Chair

Second Advisor

Rennolds Ostrom, Ph.D.

Third Advisor

Ajay Sharma, Ph.D.

Fourth Advisor

Sun Yang, Ph.D.


The practice of precision medicine considers a variety of sources of information to optimize patient care. Factors such as patient demographics, clinical history, and lab test values have well understood effects on treatment outcomes and influence decision making. However, effective inclusion of biomolecular data such as protein expression and DNA sequencing data within the practice of precision medicine needs continued study. Informatics tools offer solutions to allow these complex data sources to be effectively embraced. Utilization of informatics tools to visualize data pertaining to the gene selection practices of pharmacogenomic (PGx) tests effectively communicated large amounts of information into concise heatmaps. After a thorough search identifying potential PGx tests, their detection rates were assessed based on their gene targets and the genomic frequencies of various ethnic groups. Detection rates were defined as the proportion of a prospective ethnic population where PGx tests selected both variants within genotypes of requiring alterations in medication therapy. Detection rates had high levels of variance between different assays and ethnic groups. Our results strongly support the practice of clinicians considering a patient’s ethnic background when selecting a PGx test that is right for them to ensure effective testing. In addition to genetic test selection, applied informatics tools allow for better utilization of biomolecular information in patient prognosis assessment and therapy selection. We demonstrated this on a cohort on non-small cell lung cancer patients receiving immune checkpoint inhibitor (ICI) therapy. Through multivariate statistical models and vi survival analyses, we demonstrated the impact of various clinical and biomolecular variables on patient survival. Our results showed patients experiencing immune related adverse events (irAEs) and their timing had a significant impact on patient survival time. Additionally, we demonstrated the timing of genotype targeted tyrosine kinase inhibitor therapy relative to ICI therapy has a significant impact on patient survival time as well. Variables with less understood associations with patient survival were effectively contextualized with common clinical variables within multivariate modeling approaches. Continued implementation of informatics approaches is vital to effectively embrace a precision medicine approach in patient care.

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.



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