Epigenetic profiling for prognostic stratification and personalized therapy in breast cancer

The rising incidence of breast cancer and its heterogeneity necessitate precise tools for predicting patient prognosis and tailoring personalized treatments. Epigenetic changes play a critical role in breast cancer progression and therapy responses, providing a foundation for prognostic model development.

We developed the Machine Learning-derived Epigenetic Model (MLEM) to identify prognostic epigenetic gene patterns in breast cancer. Using multi-cohort transcriptomic datasets, MLEM was constructed with rigorous machine learning techniques and validated across independent datasets. The model's performance was further corroborated through immunohistochemical validation on clinical samples.

MLEM effectively stratified breast cancer patients into high- and low-risk groups. Low-MLEM patients exhibited improved prognosis, characterized by enhanced immune cell infiltration and higher responsiveness to immunotherapy. High-MLEM patients showed poorer prognosis but were more responsive to chemotherapy, with vincristine identified as a promising therapeutic option. The model demonstrated robust performance across independent validation datasets.

MLEM is a powerful prognostic tool for predicting breast cancer outcomes and tailoring personalized treatments. By integrating epigenetic insights with machine learning, this model has the potential to improve clinical decision-making and optimize therapeutic strategies for breast cancer patients.