Unveiling ESR1's Network Centrality through Multi-Omics Fusion and AI: Implications for Next-Generation Endocrine Therapies

Abstract

Estrogen Receptor 1 (ESR1) remains a cornerstone therapeutic target in estrogen receptor-positive (ER+) breast cancer, driving ~70% of cases through dysregulated endocrine signalling. This study leverages the Swalife PromptStudio platform, an AI-native framework integrating large language models for structured target deconvolution, to generate a comprehensive multi-dimensional profile of ESR1. Through literature mining, multi-omics integration (transcriptomics, proteomics, metabolomics), gene ontology/pathway mapping, protein interaction networks, and genetic evidence analysis, we affirm ESR1's exceptional biomarker stability, network centrality, and translational relevance. Key findings include near-perfect fold-change consistency across omics layers (r ≈ 0.78 mRNA-protein correlation), dominant enrichment in estrogen signalling with crosstalk to PI3K-Akt/MAPK/mTOR pathways, and high-frequency somatic mutations (e.g., Y537S, D538G) in ~35% of AI-resistant tumours. These insights highlight ESR1's role in resistance mechanisms, metabolic reprogramming, and post-translational modifications, underscoring opportunities for next-generation therapies like oral SERDs (e.g., Elacestrant), PROTACs, and combination strategies with CDK4/6 inhibitors. This AI-accelerated approach exemplifies rapid, reproducible target validation, paving the way for precision oncology advancements