Abstract 5052: Large-scale genomic analysis of mutation signatures reveals pathway alternations linked to breast cancer prognosis

Abstract Introduction: Advancing breast cancer treatment demands a thorough understanding of the genetic and molecular mechanisms that drive disease progression. Our study aims to explore pathway-wide mutational patterns to uncover molecular interactions that influence survival outcomes. Methods: We integrated mutation data from three major breast cancer databases, comprising a total of 4,586 samples and over 25,000 genes. We considered disruptive mutations for survival analysis. We applied Cox proportional hazard regression to link gene mutations and altered molecular pathways with patient outcomes. Results: Our analysis identified 17 genes with significant associations with relapse-free survival, with the most impactful genes being TP53 (HR: 2.04, p: 4.65×10-33), CARD11 (HR: 2.59, p: 1.54×10-5), and PIK3R1 (HR: 2.27, p: 3.66×10-5). The most significant KEGG pathways affecting relapse-free survival were MicroRNAs in cancer (hsa05206, HR:2.48, p: 2.69×10-32), Hepatocellular carcinoma (hsa05225, HR: 2.39, p: 8.88×10-30), and Breast cancer (hsa05224, HR:2.38, p: 1.78×10-28). We also identified significant patterns of mutual exclusivity and co-mutation. Pairs such as GATA3-TP53 (OR: 0.201, p: 1.42×10-37), CDH1-TP53 (OR: 0.336, p: 3.66×10-21), and AKT1-PIK3CA (OR: 0.199, p: 4.97×10-13) were mutually exclusive, while MAP3K1-PIK3CA (OR: 2.83, p: 4.62×10-23), CBFB-GATA3 (OR: 4.67, p: 3.37×10-18), and AHNAK2-MUC16 (OR: 3.39, p: 1.35×10-15) emerged as co-mutated gene pairs. Finally, an online platform (https://server2.kmplot.com/breast) has been developed to facilitate future studies designed to validate pathway-specific results. Conclusions: In summary, by compiling a large-scale breast cancer database, we identified disruptive mutation patterns associated with altered survival outcomes. Out new tool provides a valuable resource for future research and treatment development. Citation Format: Mate Posta, Balazs Gyorffy. Large-scale genomic analysis of mutation signatures reveals pathway alternations linked to breast cancer prognosis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 5052.