A Method for Identification of Pancreatic Cancer through Methylation Signatures in Cell-Free DNA

Pancreatic cancer has high mortality rates in comparison to other cancers due to limited treatment options and challenges in detecting the disease. Early diagnosis is difficult; successful outcomes are directly tied to detection of the cancer before it can spread throughout the body. Evaluation of circulating cell-free DNA (cfDNA), specifically, detection of circulating tumor DNA (ctDNA), is being explored as an approach for non-invasive ‘liquid biopsy’ that can be deployed widely and cost-effectively to screen for early signs of disease. DNA methylation signatures found in cfDNA can serve as a biomarker for detection of cancer. Previous efforts to detect pancreatic cancer using cfDNA showed limited sensitivity for detection. This manuscript describes work to develop a method for early detection of pancreatic cancer in circulating cell-free DNA using publicly available data from The Cancer Genome Atlas (TCGA) and the Gene Expression Omnibus (GEO). The work includes the development of methods for simulation of samples and cell-free DNA data at multiple ctDNA concentrations as well as a pilot implementation and evaluation of a machine learning model for automated classification of cfDNA samples. Chapter 1 introduces background of pancreatic cancer, cfDNA, liquid biopsy, DNA methylation and DNA methylation in cancer as well as a review of previous efforts to develop early detection applications. Chapter 2 describes the identification of DNA methylation markers that distinguish pancreatic tumor from normal pancreas and blood using publicly available data from the TCGA and GEO. In addition, the chapter outlines the development of a machine learning model to classify samples as tumor or normal based on these markers. Chapter 3 gives an overview of the challenges of genetic data simulation and the development of a novel tool, Heisenberg, for simulating DNA methylation data and cfDNA methylation data. This chapter also illustrate the use of Heisenberg to simulate normal blood samples and pancreatic cancer cfDNA samples. Chapter 4 describes the development of a neural network classification model for detection of pancreatic tumor in different concentrations of cfDNA using simulated samples. The chapter also reports the detection performance of the model using different model training strategies.

Name thesis:degree_name

Ph.D. (Doctor of Philosophy)

Level thesis:degree_level

Doctoral

Discipline thesis:degree_discipline

Biomedical and Health Informatics (UMKC)

Grantor

University of Missouri--Kansas City

Year dc:date.issued

2022

Author dc:creator

• Miller, Neil Andrew

Advisor dc:contributor.advisor

• Wyckoff, Gerald J.

Handle dc:identifier.uri

https://hdl.handle.net/10355/91328

OAI identifier oai:identifier

oai:mospace.umsystem.edu:10355/91328