Integrating omics and histopathology profiles for precision medicine
Abstract/Contents
- Abstract
- Precision medicine is an approach that accounts for individual differences to guide disease prevention and treatment. Previous research has identified associations between individual omics differences - such as genomic, transcriptomic, or proteomic variations - and a number of disease phenotypes. However, the molecular processes leading to many complex diseases remain obscure. In addition, the clinical value of integrating omics with pathology evaluation is yet to be established. To address this gap, this work aims to elucidate the molecular and histomorphology patterns underpinning clinical phenotypes of non-small cell lung cancer, ovarian cancer, and bronchopulmonary dysplasia. This dissertation has three parts: (1) the analysis of omics patterns in complex diseases, (2) the associations between quantitative histopathology image features and patient phenotypes, and (3) the integration of histopathology images and omics profiles to gather further biomedical insights. In the first part, I describe how omics information can guide precision medicine with two examples: understanding the biological processes involved in bronchopulmonary dysplasia through exome sequencing and predicting ovarian cancer patients' response to chemotherapy with tumor proteomics information. The second section demonstrates the utility of a fully automated bioinformatics pipeline for analyzing histopathology images and predicting the prognosis of non-small cell lung cancer patients, with the results validated in an independent cohort. In the last part, I describe the integration of omics profiles and histopathology findings to better understand the biological pathways underpinning different microscopic morphology in lung adenocarcinoma. I conclude that this integration yields better clinical predictions for precision medicine. These results suggest that the inter-individual differences in omics profiles and histopathology patterns not only provide useful information for clinical decision-making, but also reveal the molecular mechanisms driving the diverse tumor cell morphology. Through the integration of histopathology and omics studies, we can understand the biological processes associated with different disease phenotypes, identify the inter-individual differences in prognosis, and contribute to personalizing treatment plans for each patient. This approach will reduce the cost of healthcare and improve the quality of life in numerous patients.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2016 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Yu, Kun-Hsing |
|---|---|
| Associated with | Stanford University, Program in Biomedical Informatics. |
| Primary advisor | Altman, Russ |
| Primary advisor | Snyder, Michael, Ph. D |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Snyder, Michael, Ph. D |
| Thesis advisor | Ré, Christopher |
| Thesis advisor | Rijn, Matt van de |
| Advisor | Ré, Christopher |
| Advisor | Rijn, Matt van de |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Kun-Hsing Yu. |
|---|---|
| Note | Submitted to the Program in Biomedical Informatics. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2016. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2016 by Kun-Hsing Yu
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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