Unveiling Pathogenesis and Therapeutic Targets of Metastatic Breast Cancer Through Sparse Modelling of Genomic Landscape

Abstract

Abstract Breast cancer is a heterogeneous disease and ranks as one of the most lethal and frequently detected disease in the world. It poses significant challenges for precision therapy. To better decipher the patterns of heterogeneous nature in human genome and converge them into common functionalities, mutational signatures are introduced to define the types of DNA damage, repair and replicative mechanisms shaping the genomic landscape of each cancer patient. In this study, we developed an upgraded deep learning (DL) model, based on pruning technology to improve model generalization through deep sparsity. We applied it to patient whole genome sequencing (WGS) samples, and identified statistically significant mutational signatures associated with metastatic progression using Shapley additive explanations (SHAP). We also employed gene cumulative contribution abundance analysis to link the mutational signatures with relevant genes uncovering the shared molecular mechanisms behind tumorigenesis and metastasis of each patient and potentially lead to novel therapeutic target identification. Our study illustrates that our approach is an effective tool for discovering clinically meaningful mutational signatures in metastatic breast cancer (MBC) and relating them directly to relevant biological functions and gene targets. These findings could facilitate the development of novel therapeutic strategies and improve the clinical outcomes for individual patients.