Elucidating Chemotherapy Resistance in Breast Cancer Through Advanced Subpathway Analysis Algorithm: A Novel Approach to Topological Interpretation of Transcriptomic Data

Abstract

Chemotherapy resistance in breast cancer, particularly Triple-Negative Breast Cancer (TNBC), poses a significant challenge to effective treatment, contributing to high mortality rates. This thesis investigates the molecular mechanisms underlying chemoresistance, focusing on enhancing the granularity of pathway analysis through an innovative sub-pathway analysis algorithm. Traditional pathway analyses, while providing fundamental insights, often overlook the intricate and individual-specific nature of chemoresistance. The research introduces an advanced sub-pathway analysis algorithm that dissects larger pathways into smaller, more detailed sub-pathways, allowing for precise exploration of molecular interactions driving chemoresistance. The methodology involves a comparative analysis of transcriptome profiles from breast cancer patients before and after chemotherapy, utilizing both established and new sub-pathway analytical techniques. This integrative approach aims to uncover previously unrecognized mechanisms of resistance and identify potential biomarkers for chemoresistance. Furthermore, the thesis presents the development of a new algorithm, i-Subway, designed to conduct sub-pathway analyses at the individual sample level. This algorithm incorporates both inhibitory and inductive relationships within sub-pathways and integrates the empirical Bayes statistical model with the topological structure of the sub-pathway, significantly improving computational efficiency. When applied to transcriptomic data from 56 breast cancer cell lines, i-Subway revealed substantial variation at the sub-pathway level, providing deeper insights into the molecular basis of chemoresistance. Overall, this thesis aims to enhance the understanding of the specific pathways and sub-pathways altered in response to chemotherapy, offering new insights into the molecular mechanisms of chemoresistance in breast cancer. The findings are expected to facilitate the identification of novel therapeutic targets and contribute to the development of more effective, individualized treatment strategies.