The enhancer-state landscape of breast cancer subtypes: an integrative epigenomic map of regulatory architecture and clinical associations

Breast cancer (BRCA) is a highly heterogeneous malignancy comprising multiple molecular subtypes with distinct transcriptional and clinical characteristics. Although subtype-specific transcription factors have been well characterized, the enhancer-state architecture underlying subtype identity remains incompletely understood. We performed an integrative multi-omics analysis of breast cancer cell lines and patient datasets by combining histone-mark ChIP-seq, ATAC-seq, DNase-seq, GRO-seq, bulk and single-cell RNA-seq, and clinical transcriptomic data. Enhancers were classified into active, poised, and inactive states based on histone modifications and chromatin accessibility. Subtype-specific enhancers, enhancer-associated genes, transcription factor networks, clinical associations, and responses to BET inhibition were systematically evaluated. We constructed a comprehensive enhancer-state landscape across Basal-like, Her2-enriched, Luminal A, and Luminal B-like breast cancer subtypes. Active enhancers showed stronger subtype-discriminatory power than promoter-associated chromatin features and were closely linked to subtype-specific gene expression programs. Luminal enhancers were enriched for ESR1, FOXA1, and GATA3, whereas Basal and Her2 enhancers were associated with distinct transcription factor signatures, including STAT3, SOX10, MYC, and AP-1. Integration with clinical datasets identified enhancer-associated genes, including TGFB2, TGFB3, INPP4B, and BRD4, with subtype-associated prognostic relevance. Analysis of JQ1-treated MCF-7 cells further suggested that enhancer-associated transcriptional programs may be sensitive to BET inhibition. This study delineates the enhancer-state landscape of breast cancer subtypes and identifies associations among subtype-specific enhancer patterns, transcription factor signatures, gene expression programs, and selected clinical features. These findings provide an integrative epigenomic framework for characterizing breast cancer subtype-associated regulatory patterns and prioritize candidate enhancer-associated relationships for future functional validation.