Evolutionary Overview and Future Perspectives: ESR1 Mutations, Liquid Biopsy, and Artificial Intelligence for a New Era of Personalized Medicine in ER+ Breast Cancer

Mol Diagn Ther. 2025 Nov;29(6):773-787. doi: 10.1007/s40291-025-00811-8. Epub 2025 Aug 30.

Abstract

ESR1 gene mutations represent one of the main mechanisms of acquired resistance to endocrine therapy (ET) in estrogen receptor-positive (ER+) breast cancer. The introduction of liquid biopsy as a minimally invasive technique for analyzing circulating tumor DNA (ctDNA) has opened new avenues for real-time mutation monitoring and personalized treatment strategies. This review explores the clinical relevance of ESR1 mutations in endocrine resistance, the potential of liquid biopsy for early detection and monitoring, and the integration of advanced sequencing technologies and artificial intelligence to improve diagnostic accuracy. Preclinical and clinical studies on key mutations (D538G, Y537S) were analyzed, emerging technologies [(next-generation sequencing (NGS), digital droplet PCR (ddPCR), Cancer Personalized Profiling by deep Sequencing (CAPP-Seq), Targeted Digital Sequencing (TARDIS)] were compared, and survival data from seven major studies were summarized to assess the impact of ESR1 mutations on progression-free survival (PFS) and overall survival (OS). The results show that these mutations, particularly those affecting the ligand-binding domain, are associated with reduced efficacy of aromatase inhibitors and increased tumor aggressiveness. Liquid biopsy proves useful for early detection of resistance mutations and dynamic disease monitoring, but its clinical implementation is limited by low ctDNA levels, technological variability, and the lack of standardized clinical cut-offs. Integration with tissue biopsy, radiomics, and artificial intelligence (AI)-based platforms enhances its clinical utility and prognostic value. In conclusion, liquid biopsy, when combined with advanced technologies and predictive tools, represents an innovative resource for the personalized management of ER+ breast cancer, with the potential to guide timely therapeutic interventions and improve long-term survival.

Publication types

  • Review

MeSH terms

  • Artificial Intelligence*
  • Biomarkers, Tumor / genetics
  • Breast Neoplasms* / diagnosis
  • Breast Neoplasms* / drug therapy
  • Breast Neoplasms* / genetics
  • Breast Neoplasms* / metabolism
  • Breast Neoplasms* / mortality
  • Circulating Tumor DNA / genetics
  • Drug Resistance, Neoplasm / genetics
  • Estrogen Receptor alpha* / genetics
  • Female
  • High-Throughput Nucleotide Sequencing
  • Humans
  • Liquid Biopsy / methods
  • Mutation*
  • Precision Medicine* / methods

Substances

  • Estrogen Receptor alpha
  • ESR1 protein, human
  • Biomarkers, Tumor
  • Circulating Tumor DNA