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Updated: Jul 12, 2026

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer
10:36

Systems Biology of Metabolic Regulation by Estrogen Receptor Signaling in Breast Cancer

Published on: March 17, 2016

Application of aromatase inhibitors in endocrine responsive breast cancers

Paul Goss1, Melinda Wu

  • 1Massachusetts General Hospital, 55 Fruit Street, LRH 302, Boston, MA 02114, USA. pgoss@partners.org

Breast (Edinburgh, Scotland)
|August 28, 2007
PubMed

Insights

Aromatase inhibitors are key in breast cancer treatment for post-menopausal women. Research is ongoing to optimize their use, duration, and effectiveness, especially in pre-menopausal women and in overcoming resistance.

Area of Science:

  • Oncology
  • Endocrinology
  • Pharmacology

Background:

  • Aromatase inhibitors (AIs) are standard adjuvant therapy for early-stage breast cancer in post-menopausal women.
  • The role of AIs in pre-menopausal women is under investigation.
  • Unanswered questions include optimal sequencing with tamoxifen, duration, and agent selection.

Purpose of the Study:

  • To review the current status and future directions of aromatase inhibitor therapy in breast cancer.
  • To explore strategies for overcoming de novo and acquired resistance to AIs.
  • To highlight the potential of pharmacogenomic and genomic signatures for personalized therapy.

Main Methods:

  • Review of clinical trial data, including the MA17 trial and its follow-on studies.
  • Exploration of novel therapeutic strategies, such as combining AIs with fulvestrant or targeting cell signaling pathways (erbB2, IGF1R, mTOR).
  • Discussion of the role of biomarkers in patient selection.

Main Results:

  • Extended AI therapy beyond 5 years shows benefits, with ongoing trials exploring prolonged durations.
  • Strategies to overcome resistance include enhancing estrogen blockade and targeting collaborating pathways.
  • Pharmacogenomic and genomic signatures are emerging as predictive factors.

Conclusions:

  • Further research is needed to define optimal AI treatment paradigms.
  • Personalized medicine approaches using biomarkers will be crucial for selecting patients and improving treatment outcomes.
  • Targeting resistance mechanisms and optimizing drug combinations hold promise for enhancing efficacy.

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