Doctor of Philosophy, Case Western Reserve University, 2022, Systems Biology and Bioinformatics

The evolution of therapeutic resistance in cancer is a complex and nearly inescapable process driven by evolutionary dynamics. As the genomic characterization of cancer has provided insight into the mechanisms of oncogenesis, tumor growth, and metastasis we have developed targeted therapeutics to provide personalized treatment based on mutation status. Yet, most cancer patients are not helped by these drugs, with just over 7\% of cancer patients in the United States benefiting from genome-driven care in 2020. In this work, we propose that an evolutionary-driven problem requires an evolutionary-inspired solution. We present a novel method for extracting therapeutic response biomarkers in cancer, and demonstrate its utility in large, publicly-available datasets, tightly-controlled in silico experiments, and translational efforts. Our procedure exploits principles of convergent evolution to find patterns across tumors with distinct evolutionary histories and mutational profiles. First, we use this signature extraction method to predict collateral treatment response in Ewing's sarcoma cell lines. While evolving resistance to standard of care treatment across multiple evolutionary replicates of a Ewing's sarcoma cell line, we compared gene expression between responders and non-responders to a variety of second-line treatments. Differential gene expression results were compiled to generate biomarkers of therapeutic response which are hypothesized to be clinically relevant, but require further validation. Next, we use epithelial cell lines from the Genomics of Drug Sensitivity in Cancer (GDSC) database to extract the Cisplatin Response Signature (CisSig). CisSig is predictive of cisplatin IC50 in GDSC cell lines, clinical trends in tumor sample databases, and survival outcomes in patients who received cisplatin-containing chemotherapy. Finally, we propose translational efforts to move two gene expression signatures, CisSig and the genome-adjusted radiation dose (GARD), (open full item for complete abstract)

Committee: David Lodowski (Committee Chair); Jacob Scott (Advisor); Drew Adams (Committee Member); Mark Chance (Committee Member) Subjects: Bioinformatics; Oncology; Radiation; Systems Science

Doctor of Philosophy, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects: Education

Doctor of Philosophy, The Ohio State University, 1971, Graduate School

Committee: Not Provided (Other) Subjects: Education

Master of Arts (MA), Bowling Green State University, 2015, Psychology/Industrial-Organizational

This study investigated potential causes of extant group mean differences of self-reported levels of burnout across sex. Based on the origins of burnout as an interpersonal construct, combined with research showing differences in how men and women perceive and resolve interpersonal conflict and stress, it was hypothesized that statistical artifacts, namely violations of the measurement invariance assumption, were present. It was further hypothesized that these artifacts were causing differences in the prevalence of self-reported burnout and that removing them would reduce the size of such differences. Participants (n = 579) completed three separate measures of burnout: the Copenhagen Burnout Inventory, the Oldenburg Burnout Inventory, and the Maslach Burnout Inventory, as well as several measures of nomological correlates of burnout. The data were analyzed using confirmatory factor analysis and item response theory approaches to measurement invariance. Results indicated that no violations of measurement invariance were present. Implications for future research and practice are discussed.

Committee: Michael Zickar (Committee Chair); Steve Jex (Committee Member); William O'Brien (Committee Member) Subjects: Occupational Psychology; Organizational Behavior; Psychological Tests; Psychology

Doctor of Philosophy (PhD), Ohio University, 2005, Educational Research and Evaluation (Education)

MCAT scores and undergraduate GPA are two screening devices that are widely used by medical schools in the U.S. to select aspiring medical students. Given their widespread use, it is important that the validities of MCAT scores and undergraduate GPA be investigated to ensure the accuracy of medical school admission decisions. This study was therefore designed to address three important aspects of validation, namely, predictive validity of MCAT subtest scores and undergraduate GPA, differential validity, and differential prediction of the MCAT subtest scores. First, predictive validity was evaluated as an index of the relationship between the predictors, MCAT subtest scores and undergraduate GPA, and the criterion, first-year medical school GPA. Second, differential validity was assessed by comparing the magnitude of validity coefficients obtained as the correlation between first-year medical school GPA and MCAT subtest scores for men and women and for White, Black, Asian, and Hispanic medical students. Third, differential prediction was examined by testing for differences in the regression systems obtained for the different subgroups of examinees. The sample used in the study consisted of 3,187 students drawn from 1992 and 1993 cohorts of 14 medical schools. Statistical procedures utilized in the research included regression analysis, Fisher's z transformations, F-ratio test of equality of standard errors of estimate, and ANCOVA tests of equality of regression slopes and intercepts. Results obtained showed moderately high correlations between the predictors and the criterion. MCAT subtest scores and previous grades were individually good predictors of medical school freshman grades. The combination of MCAT subtest scores and undergraduate GPA was, however, a more powerful indicator of performance in the first-year of medical school. Differential validity results showed that in most cases women had higher validity coefficients compared to men. With regards to differe (open full item for complete abstract)

Committee: George Johanson (Advisor) Subjects: Education, Tests and Measurements