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Abstract
Breast cancer remains the most prevalent malignancy among women globally, with endocrine therapy representing the cornerstone of treatment for hormone receptor-positive subtypes. However, the emergence of therapeutic resistance poses significant clinical challenges. This narrative review presents a comprehensive computational framework integrating mathematical modeling, systems biology, and clinical pharmacology to understand hormonal dynamics in breast cancer progression and treatment response.
References
Li, X., et al. (2014). Mathematical models of the transitions between endocrine therapy responsive and resistant states in breast cancer. Journal of The Royal Society Interface, 11(96), 20140206. https://doi.org/10.1098/rsif.2014.0206
Barillot, E., et al. (2012). Computational Systems Biology of Cancer. Chapman & Hall/CRC Mathematical and Computational Biology Series.
Altrock, P. M., et al. (2015). The mathematics of cancer: integrating quantitative models. Nature Reviews Cancer, 15(12), 730-745.
Cristofanilli, M., et al. (2023). Estrogen/HER2 receptor crosstalk in breast cancer. npj Breast Cancer, 9, 33.
National Cancer Institute. (2024). Breast Cancer Biomarkers. https://www.cancer.gov/types/breast/diagnosis/breast-cancer-biomarker-tests
Baum, M., et al. (2002). Aromatase inhibitors take on tamoxifen. Nature Medicine, 8(12), 1341-1343.
Dowsett, M., & Howell, A. (2003). Aromatase inhibitors in breast cancer. New England Journal of Medicine, 348(24), 2431-2442.
Owise UK. (2021). Tamoxifen vs. Aromatase Inhibitors – How Do They Work. https://owise.uk/tamoxifen-aromatase-inhibitors/
National Foundation for Cancer Research. (2018). Hormone Therapy for Cancer Influencing Breast Cancer. https://www.nfcr.org/blog/blog-hormone-therapy-for-cancer/
Wang, Y., et al. (2020). Cancer modeling: From mechanistic to data-driven approaches. Current Opinion in Systems Biology, 25, 1-8.
Claret, L., et al. (2016). Computational oncology — mathematical modelling of drug regimens for precision medicine. Nature Reviews Clinical Oncology, 13(4), 242-254.
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