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dc.contributor.advisorVarelas, Xaralabosen_US
dc.contributor.authorZhang, Jiaruien_US
dc.date.accessioned2020-06-22T18:33:33Z
dc.date.issued2020
dc.identifier.urihttps://hdl.handle.net/2144/41252
dc.description.abstractLung cancer is the leading cause of cancer mortality in the US and the world, largely due to the challenges with early detection and precision management of aggressive cancer. We previously derived and validated bronchial and nasal epithelial gene expression biomarkers to detect lung cancer among individuals undergoing clinical workup for suspect of lung cancer. However, there are continuing challenges and needs to better understand lung cancer airway biology and ultimately impact clinical management: 1. Whether airway genomic classifiers could be developed to detect cancer among patients with indeterminate pulmonary nodules; 2. What are the airway cellular and molecular subtypes and their abilities to improve lung cancer diagnosis; 3. Whether molecular and histological subtype profiling based on lung adenocarcinoma gene expression would impact pre-/post-surgical management by indolence and aggressiveness prediction. To fulfill above goals, I first developed a cancer biomarker based on the nasal airway gene expression alterations, and improved clinical model prediction among patients with indeterminate pulmonary nodules. Next, I leveraged both bulk and single cell bronchial airway gene expressions from patients of different lung cancer subtypes, and identified the molecular and cellular changes associated with adenocarcinoma vs. squamous cell carcinoma. This finding facilitated the development of a lung cancer subtype biomarker that improved the diagnostic accuracy of the previous lung cancer classifier. Finally, I leveraged tumor gene expression data from clinical stage I lung adenocarcinomas from a screening population, and identified solid-, micropapillary- and cribriform-specific gene signatures. A classifier predictive of aggressive histologic features was developed with potential to predict histologic aggressiveness from pre-surgical tumor biopsies where all histologic patterns may not be represented. Such a biomarker may be useful in guiding clinical decision making including extent of surgical resection. Findings and discussions in this dissertation will discuss the potential for these biomarkers to have clinical utility in patients with or at risk for lung cancer.en_US
dc.language.isoen_US
dc.subjectBioinformaticsen_US
dc.titleGenomic biomarker development to impact clinical management of patients at risk for lung canceren_US
dc.typeThesis/Dissertationen_US
dc.date.updated2020-06-20T04:01:24Z
dc.description.embargo2022-06-19T00:00:00Z
etd.degree.nameDoctor of Philosophyen_US
etd.degree.leveldoctoralen_US
etd.degree.disciplineMolecular and Translational Medicineen_US
etd.degree.grantorBoston Universityen_US
dc.identifier.orcid0000-0001-9639-4056


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