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search.filters.applied.f.advisor: McDonald, John F. × Author: Ban, Dongjo × Item Type: Publication × search.filters.applied.f.isOrgUnitOfPublicationTitle: College of Sciences ×

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A holistic approach to improving ovarian cancer care

2023-07-17 , Ban, Dongjo

Ovarian cancer (OC), often referred to as the "silent killer" due to its elusive early-stage symptoms and frequent late diagnoses, remains a significant public health challenge. The primary objective of this research is to navigate the intricate landscape of OC at the genomic and metabolomic levels using high-throughput technologies. This exploration strives to uncover potential strategies for early detection and treatment improvement, thereby addressing this persistent health concern. In the initial phase of the study, the genomic complexity of OC is unraveled through an analysis of the tumor mutation burden (TMB) and patterns of copy-number alterations (CNAs). The investigation reveals a higher TMB in localized tumors and cancer-related genes compared to non-cancer genes. We observed that impaired DNA-repair mechanisms play a pivotal role in elevating TMB levels. A notable finding is the differential selective pressure patterns, represented by dN/dS ratio estimates, between early- and late-stage OC. Further, the impact of CNAs on OC patients was analyzed, showing a prevalence of amplification events over deletion ones and a higher number of affected genes in the early-stage group. Although CNAs were not found to be higher in cancer-associated genes, the study identifies a preference for amplification in oncogenes and deletion in tumor suppressor genes upon investigating driver regions. The latter phase of the research emphasizes the role of metabolomics in detecting early-stage OC. Machine learning (ML) approaches were employed to examine high-throughput serum metabolomic profiles from OC patients and non-cancerous individuals from various geographical locations. The resulting classifiers exhibited promising predictive potential, thus emphasizing the utility of metabolomics for early OC detection. Particularly, the emergence of lipid or lipid-like molecules as potential markers underscores their significance in OC detection. Collectively, these findings accentuate the potential of an integrated approach in developing personalized cancer management strategies, taking into account the unique variations observed in patients. This paves the way for clinically identifying high-risk individuals for more frequent monitoring and tailoring appropriate treatment options for optimal patient outcomes. Given the growing volume of data and the continuous advancements in technology, such comprehensive approaches can augment survival rates and ameliorate the quality of life for OC patients.

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