Epigenetic profile drives accurate survival prediction in breast cancer via a multi-omics machine learning model

Abstract Accurate overall survival (OS) prediction is key for personalized treatment in breast cancer, but mutation burden alone is insufficient. To improve prognostic accuracy, we integrated genomic, transcriptomic, proteomic, epigenetic, and clinical features from 802 breast cancer patients to develop BANDOL (Breast cancer Analysis with Neoplastic Data and Omics Learning), a Random Survival Forest model. BANDOL correctly predicted survival ranking in 73% of patient pairs and outperformed mutation-based models (time-dependent AUC: 0.9–1 vs. 0.4–0.9). Immune activation signatures correlated with a longer OS after therapy, while a shorter OS was linked to TREM2⁺ myeloid cells, B cells, and leptin signaling. The transferability of the model was further explored in three independent TCGA cohorts from distinct cancer types (uterine, ovarian and lower-grade glioma), where moderate predictive performance was maintained despite biological differences between tumors. Epigenetic features were the strongest OS predictors for BANDOL. Current therapies may be combined with strategies to target the methylations of ME3, PPARG, OLIG3 and SLC25A22 genes. This study demonstrates that multi-omics integration via machine learning enhances survival prediction and reveals actionable biomarkers. Abstract Figure