The Utilization and Optimization of Omics Trait Prediction Models Within and Across Diverse Populations
Date of Award
Master of Science (MS)
Bioinformatics & Computational Biology
Most cancer chemotherapeutic agents are ineffective in a subset of patients; thus, it is important to consider the role of genetic variation in drug response. Lymphoblastoid cell lines (LCLs) derived from 1000 Genomes Project populations of diverse ancestries are a useful model for determining how genetic factors impact variation in cytotoxicity. In our study, LCLs from three 1000 Genomes Project populations of diverse ancestries were previously treated with increasing concentrations of eight chemotherapeutic drugs and cell growth inhibition was measured at each dose with half-maximal inhibitory concentration (IC50) or area under the dose-response curve (AUC) as our phenotype for each drug. We conducted genome-wide (GWAS), transcriptome-wide (TWAS), protein-based association studies (PAS) within and across ancestral populations. We identified four unique loci with GWAS, three genes with TWAS, and seven proteins with PAS significantly associated with chemotherapy-induced cytotoxicity within and across ancestral populations. For etoposide, increased STARD5 predicted expression associated with decreased etoposide IC50 (p = 8.5 x 10-8). Functional studies in A549, a lung cancer cell line, revealed that knockdown of STARD5 expression resulted in decreased sensitivity to etoposide following exposure for 72 (p = 0.033) and 96 hours (p = 0.0001). By identifying loci, genes, and proteins associated with cytotoxicity across ancestral populations, we strive to understand the genetic factors impacting the effectiveness of chemotherapy drugs and to contribute to the development of future cancer treatment.
Mulford, Ashley, "The Utilization and Optimization of Omics Trait Prediction Models Within and Across Diverse Populations" (2021). Master's Theses. 4362.
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Copyright © 2021 Ashley Mulford