Deciphering Breast Cancer Complexity: Harnessing Spatial Transcriptomics and AI for Personalized Therapeutic Strategies

Abstract

Breast Cancer, particularly Triple-Negative Breast Cancer (TNBC), remains a formidable challenge due to its aggressive nature, lack of targeted therapies, and poor prognosis. This research addresses the critical need for more accurate prognostic markers by integrating spatial transcriptomics with artificial intelligence (AI) to explore the spatial heterogeneity of gene expression within TNBC tumors. Spatial transcriptomics offers a high-resolution view of the tumor microenvironment, preserving the spatial context of gene expression, while single-cell RNA sequencing (scRNA-seq) provides detailed insights into the cellular composition of the tumors. By identifying and analyzing differentially expressed genes (DEGs) across spatial and single-cell datasets, this study aims to uncover key biomarkers that could serve as therapeutic targets and improve patient outcomes. Machine learning models, including XGBoost and Support Vector Machines (SVM), were employed to develop predictive models for cancer classification, disease staging, and prognosis. These models demonstrated high accuracy, enhancing the understanding of TNBC's complex molecular landscape and supporting the development of personalized treatment strategies. The findings highlight the potential of integrating spatial transcriptomics with AI to revolutionize cancer research, offering new avenues for precision medicine in TNBC.