Unveiling pathogenesis and therapeutic targets of metastatic breast cancer through sparse modeling of genomic landscape

Abstract

Breast cancer is one of the most lethal and frequently detected diseases in the world. Its heterogeneity poses significant challenges for precision therapy. To better decipher patterns in the human genome given its heterogenous nature and have them converge 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 a deep learning (DL) model, based on pruning technology to improve model generalization through deep sparsity. We applied it to patient-level 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 which can 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.