Defense Date
2026
Document Type
Thesis
Degree Name
Master of Science
Department
Bioinformatics
First Advisor
Preetam Ghosh
Second Advisor
Michael Rosenberg
Third Advisor
LaMont Cannon
Abstract
Breast cancer is a heterogeneous disease driven by complex genomic and transcriptomic alterations, making accurate phenotype prediction a challenging task. Recent advances in genomic foundation models and representation learning provide new opportunities to extract biologically meaningful features directly from sequencing data. The objective of this study was to develop and evaluate a multimodal framework integrating whole genome sequence (WGS) and RNA sequencing (RNA-Seq) embeddings for breast cancer classification. DNABERT-2, a DNA foundation model, was evaluated for its ability to generate genomic representations from The Cancer Genome Atlas breast cancer (TCGA-BRCA) WGS data, and the impact of downstream fine-tuning on embedding performance was assessed. Multiple autoencoder architectures were investigated to generate low-dimensional RNA-Seq embeddings of paired TCGA-BRCA data, and principal component analysis (PCA) was evaluated as a baseline dimensionality reduction approach. WGS, RNA-Seq, and integrated multimodal embeddings were subsequently evaluated for binary healthy/disease classification and multi-class PAM50 molecular subtype classification. DNABERT-2-derived WGS embeddings demonstrated strong performance for binary disease classification but provided limited predictive information for PAM50 subtype classification. RNA-Seq embeddings consistently outperformed WGS embeddings for multi-class classification, while PCA achieved comparable or superior performance to more complex autoencoder architectures. Furthermore, integrating WGS and RNA-Seq embeddings resulted in only marginal improvements compared to RNA-Seq embeddings alone, suggesting that the current WGS representations provided limited complementary information for breast cancer subtype prediction. Collectively, these findings demonstrate that increased model complexity and multimodal integration do not inherently improve predictive performance and highlight the importance of selecting biologically informative representations for genomic phenotype prediction.
Rights
© Leiliani Clark
Is Part Of
VCU University Archives
Is Part Of
VCU Theses and Dissertations
Date of Submission
8-4-2026