ORCID ID
Graduation Date
Spring 5-9-2026
Document Type
Thesis
Degree Name
Master of Science (MS)
Programs
Molecular Genetics & Cell Biology
First Advisor
Dr. Shibiao Wan
Abstract
The increasing accessibility of transcriptomic profiling has resulted in a surge of molecular pathway-based cancer subtyping in the last decade. Subtype-specific treatment and risk stratification have been demonstrated to improve survival rates and may allow for a more efficient allocation of resources. Pancreatic cancer (PaC), the third leading cause of cancer-related deaths in the United States, is known for its high intra- and inter-tumor heterogeneity. Pancreatic ductal adenocarcinoma (PDAC), the most common variant of PaC, has a very low five-year survival rate and remains difficult to treat. Conventional wet-lab approaches for PDAC subtyping (microdissection, histopathological studies, etc.) are laborious, costly, and often ineffective. Although the basal and classical subtypes of PDAC are widely accepted, high heterogeneity in clinical response and molecular features still exists within these groups. Further subtyping focused on this molecular landscape may allow for better risk stratification and personalized treatment. To address these concerns, we developed two advanced machine learning approaches (i.e., one supervised learning approach and one unsupervised learning approach) to identify PDAC subtypes based on transcriptomic data. Specifically, for the supervised learning approach, we developed a novel stacking-based learning framework, MetaPaCS, to categorize tumor samples into a previously established four-subtype scheme based on RNA-seq data. MetaPaCS demonstrated significantly better performance over existing machine learning (ML) classifiers. For the unsupervised learning approach, we created PacEnClust, a wMetaC-based framework to explore high-resolution PaC subtypes by integrating eleven different cohorts of PaC patients. PacEnClust was shown to outperform traditional clustering algorithms when finding the same number of clusters and revealed a stable five subtype scheme for PDAC. We expect our proposed approaches will provide robust frameworks for better PaC subtype characterization for accurate downstream risk assessment and personalized treatment design.
Rights
The author holds the copyright to this work and any reuse or permissions must be obtained from the author directly.
Recommended Citation
Peterson, Nicholas B., "Pancreatic Cancer Subtype Prediction Via Advanced Machine Learning Approaches" (2026). Theses & Dissertations. 1048.
https://digitalcommons.unmc.edu/etd/1048