Breast cancer, one of the most prevalent malignancies worldwide, poses a significant disease burden, and is characterized by its heterogeneity and variable patient outcomes. To advance precision medicine, novel approaches are needed to improve the interpretability of complex genomic data. This study introduces BRCASonics, a prototype tool that applies data sonification—a method of representing data through sound—to a 43-gene expression signature previously identified as predictive of breast cancer survival using The Cancer Genome Atlas Breast Cancer (TCGA-BRCA) dataset. By mapping gene expression levels to auditory properties such as pitch, rhythm, and timbre, BRCASonics transforms high-dimensional genomic data into an interactive, multisensory experience. This approach not only enhances pattern recognition and accessibility but also holds educational value by providing an engaging medium for students to explore genomic concepts. Developed as an R Shiny web application, BRCASonics allows users to explore variations in gene expression across samples through auditory cues. This work demonstrates the feasibility and utility of sonification as a complementary tool to traditional data visualization, with potential applications in both clinical interpretation and genomic education. Ultimately, this research paves the way for multisensory frameworks in data communication, offering an inclusive and intuitive paradigm for engaging with complex biological information.