Discovering and validating methylated cell-free DNA biomarkers for cancer
Abstract/Contents
- Abstract
- Methylated cell-free DNA (cfDNA) biomarkers have shown promise for blood-based cancer detection, in part because of their biological properties. Methylation of CpG dinucleotides in the promoter regions of tumor suppressor genes plays an early role in cancer development, and methylated DNA wraps around nucleosome proteins, protecting it from degradation by nucleases in the blood. Recent studies have mined high-dimensional epigenetic datasets to discover methylated cfDNA cancer biomarkers, employing statistical selection methods that may risk-rank correlative biomarkers with undefined biological relationships in a cancer type above biomarkers with known epigenetic roles. Accordingly, causative biomarkers with reproducible predictive power between patient cohorts could be filtered out during the discovery process. We hypothesized that utilizing a knowledge-based biomarker enrichment method to first decrease the dimensionality of these datasets followed by individual screening of the remaining biomarkers in the data would identify causative biomarkers that collectively embody a tumor-specific methylation signal in cfDNA. Our method, Layered Analysis for Methylated Biomarkers (LAMB), leverages meta-analysis to select an enriched set of CpG biomarker candidates in high-dimensional microarray data and then filters the biomarkers against public tissue methylation data from hundreds of cancer patients and lysed whole blood methylation data from over a thousand healthy patients. In this thesis, I will introduce LAMB and demonstrate that it can discover targeted methylated cfDNA biomarker panels that: (i) detected hepatocellular carcinoma (HCC) tumors in at-risk cirrhosis patients and (ii) differentiated prostate adenocarcinoma (PRAD) patients by their tumor's response to abiraterone acetate and docetaxel. I will also describe a targeted bisulfite sequencing assay that I created to analyze LAMB biomarkers (termed LAMB-Seq). I will demonstrate that LAMB-HCC biomarkers exhibited excellent read coverage and high-resolution methylation quantification through LAMB-Seq. Through this thesis, I will show that LAMB can be used to find cancer biomarkers that have potential for improving at-risk screening and therapy monitoring. I will also lay the groundwork for its application to other cancers and provide evidence for the benefits of using the LAMB-Seq platform to validate LAMB biomarkers.
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 | 2020; ©2020 |
| Publication date | 2020; 2020 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Author | Patnaik, Ritish |
|---|---|
| Degree supervisor | Wang, Shan X |
| Thesis advisor | Wang, Shan X |
| Thesis advisor | Altman, Russ |
| Thesis advisor | Huang, Possu |
| Degree committee member | Altman, Russ |
| Degree committee member | Huang, Possu |
| Associated with | Stanford University, Department of Bioengineering. |
Subjects
| Genre | Theses |
|---|---|
| Genre | Text |
Bibliographic information
| Statement of responsibility | Ritish Patnaik. |
|---|---|
| Note | Submitted to the Department of Bioengineering. |
| Thesis | Thesis Ph.D. Stanford University 2020. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2020 by Ritish Patnaik
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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