Validation of a protein biomarker panel for early hepatocellular carcinoma detection at the Point-of-Care
Abstract/Contents
- Abstract
- Hepatocellular carcinoma (HCC) composes about 80% of liver cancer cases, which is the second leading cause of cancer-related deaths worldwide. Majority of HCC burden is present in developing countries where hepatitis B virus (HBV) vaccination is inadequate and transmission of hepatitis C virus (HCV) is prevalent. In addition, HCC is an escalating problem in the US where liver cancer rates doubled over the last three decades due to chronic HCV infection, diabetes, and obesity. Most patients become chronically infected, progress to fibrosis and cirrhosis, and finally develop HCC. Current detection methods include ultrasound and alpha-fetoprotein (AFP) serum assay. These methods lack adequate sensitivity to catch early stage HCC, and many patients get diagnosed at later stages, resulting in suboptimal prognosis and low survival rates (< 10%). It is therefore imperative to develop a sensitive and specific multiplexed method for early HCC detection in high-risk patients. Early detection leads to early treatment, resulting in better patient prognosis and survival. We will discuss how giant magnetoresistive (GMR) sensor arrays can be used to develop a protein biomarker panel for early detection of HCC in high-risk cirrhotic patients. We show that the diagnostic power of our biomarker panel can exceed that of AFP alone. We also demonstrate significant improvement to diagnostic performance (AUC = 0.89) when combining our biomarker panel assay results with imaging results and clinically available information, including age and ethnicity. Given high prevalence of HCC in developing countries, we also designed and built an automated point-of-care GMR sensor platform to perform rapid, relatively inexpensive, and user-friendly assays. We demonstrate potential use of this platform for disease detection. By relaxing current limitations imposed by training, technical skills, time, and resources, the GMR sensor platform can bring affordable healthcare to a broader audience and extend medical technology to disadvantaged parts of the world.
Description
| Type of resource | text |
|---|---|
| Form | electronic resource; remote; computer; online resource |
| Extent | 1 online resource. |
| Place | California |
| Place | [Stanford, California] |
| Publisher | [Stanford University] |
| Copyright date | 2019; ©2019 |
| Publication date | 2019; 2019 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Ng, Elaine |
|---|---|
| Degree supervisor | Wang, Shan X |
| Thesis advisor | Wang, Shan X |
| Thesis advisor | Cochran, Jennifer R |
| Thesis advisor | Utz, Paul |
| Degree committee member | Cochran, Jennifer R |
| Degree committee member | Utz, Paul |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Elaine Ng. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2019. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2019 by Elaine Ng
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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