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Updated: Jun 21, 2025

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
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EstroGene2.0: A multi-omic database of response to estrogens, ER-modulators, and resistance to endocrine therapies in

Zheqi Li1,2, Fangyuan Chen3,4, Li Chen5

  • 1Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

Biorxiv : the Preprint Server for Biology
|July 15, 2024
PubMed

Insights

EstroGene2.0 is a new database detailing breast cancer response and resistance to endocrine therapies. It reveals diverse transcriptomic alterations in resistant models and identifies key targets for mutant estrogen receptors.

Area of Science:

  • Oncology
  • Genomics
  • Pharmacology

Background:

  • Endocrine therapies targeting estrogen receptor (ER/ESR1) are crucial for ER-positive breast cancer but face resistance.
  • Understanding resistance mechanisms is vital for improving current treatments and developing novel ER-modulators.
  • Fragmented data reporting has historically limited the impact of research findings.

Purpose of the Study:

  • To introduce EstroGene2.0, an expanded database focusing on endocrine therapy response and resistance in breast cancer models.
  • To provide a user-friendly platform for comprehensive data visualization and metadata mining of multi-omic profiling data.
  • To facilitate the investigation of molecular mechanisms underlying endocrine therapy effectiveness and resistance.

Main Methods:

  • Incorporated multi-omic profiling data from 361 experiments across 212 studies and 28 cell lines.
  • Developed a user-friendly web browser (https://estrogeneii.web.app/) for data access and visualization.
  • Conducted a meta-analysis on harmonized data to assess responses to various ER-modulators and analyze transcriptomic alterations.

Main Results:

  • Revealed significant diversity in response to different classes of endocrine modulators (SERMs, SERDs, SERCA, LDD/PROTAC).
  • Identified a contra-directional shift in ER and interferon signaling in endocrine-resistant models, which correlates with clinical data.
  • Demonstrated the clinical relevance of different ESR1 mutant model engineering approaches and identified high-confidence mutant-ER targets like NPY1R.

Conclusions:

  • EstroGene2.0 provides a valuable resource for studying breast cancer endocrine therapy response and resistance.
  • The database facilitates the discovery of novel therapeutic strategies by elucidating resistance mechanisms.
  • Findings highlight the heterogeneity of endocrine resistance and the importance of specific ESR1 mutations and signaling pathways.
Keywords:
Breast CancerDatabaseEndocrine ResistanceEstrogen ReceptorNGS Profiling

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