Breast cancer remains a leading cause of cancer-related deaths worldwide, necessitating innovative therapeutic strategies. This study integrates proteomic, transcriptomic, and functional dependency data to systematically identify tumor-specific markers and vulnerabilities in breast cancer. We analyzed mass-spectrometry-based proteomic data from 115 tumor samples and 18 matched normal tissues, quantifying 10,468 proteins. By combining overexpression and differential expression analyses, we identified 172 tumor-specific proteins, including well-characterized targets such as ERBB2, EGFR, and CCND1, as well as novel candidates like TRPS1, UBE2C, and FOXP4. Functional validation of these candidate targets was performed through CRISPR-based expression-driven dependency analysis using the BEACON method and DepMap data, which revealed both gene- and protein-level dependencies, uncovering novel protein-unique cancer vulnerabilities. Notably, protein-specific dependencies such as UBE2C and E2F3 highlight potential therapeutic targets overlooked in transcriptomic analyses. In particular, markers such as TRPS1 and UBE2C, which exhibit strong protein expression-driven dependencies, may serve as potential candidates for precision oncology approaches, guiding drug development and patient stratification. This study presents a systematically prioritized set of actionable targets, emphasizing the critical role of multi-omics integration in driving precision oncology advancements for breast cancer.