Defining The Functional Significance Of Estrogen Related Receptor Alpha In Breast Cancer

Author : Shalini Jindal
Publisher :
ISBN 13 :
Total Pages : 250 pages
Book Rating : 4.:/5 (98 download)

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Download or read book The Role of Estrogen Receptor [alpha] and the Androgen Receptor in Human Breast Cancer written by Shalini Jindal and published by . This book was released on 2016 with total page 250 pages. Available in PDF, EPUB and Kindle. Book excerpt: The androgenic signalling axis interacts with other major growth pathways in breast cancer, such as estrogen receptor (ER) signalling, and is of renewed interest due to the promise of exploiting the pathway for therapeutic benefit. The effects of signalling via the androgen receptor (AR) are pleiotropic, and there is evidence in vitro and in vivo that it can both promote and inhibit proliferation of breast epithelia, largely depending on ER expression and activation. Given this complexity, the effect of pathway modulation in individual women with breast cancer remains unclear. Therefore, the purpose of the studies undertaken in this thesis was to establish baseline parameters in terms of tissue expression of AR and apply them to meaningful clinical scenarios to better establish which population of patients might benefit from androgen pathway-targeting therapies. In the first part of the study, dual-labelling immunofluorescence was performed on a tissue microarray (TMA) containing normal breast and an array of malignant tissues representing tumour progression. AR was expressed more frequently than ER, and AR+ER- cells comprised one third of the total epithelial cell population. 26.6% of the total epithelial population were AR+ER+, 37.5% AR-ER-, and a minor proportion AR-ER+ (2.8%). There were no significant differences in AR expression (either alone or co-localised) between primary and nodal metastasis lesions, and expression remained constant in in situ, invasive, and metastatic disease. AR and ER expression therefore show remarkable but stable intratumoural heterogeneity, with implications for how individual cells might respond to therapy within the tumour population as a whole. The second part of this thesis aimed to firmly establish: a) the prognostic value of AR in two independent cohorts of patients with primary breast cancer and with long-term follow-up, and b) criteria for measurement of the biomarker to pave the way for biomarker measurement in androgen-therapy trials. AR was an independent prognostic factor in two independent cohorts of primary breast cancers tested with different antibodies, and ROC analysis established that the optimal cut-point of AR positivity was 78%. Patients with high AR expression had approximately two-fold reduced risk of cancer-related death in both cohorts, and AR expression was significantly associated with ER expression. Patients with equal or high AR:ER ratios had the best 10-year overall survival of over 80%. Although unlikely to add much to existing prognostic algorithms and approaches, establishing a simple and robust diagnostic test with an appropriate cut-point will expedite studies using androgen pathway-targeting therapies. Finally, the third part of this thesis explored the hypothesis that some of the risk of breast cancer associated with increased breast density might be associated with AR expression. Although AR expression was higher in malignant than benign disease, it was not associated with breast density; breast density is likely to be more related to cumulative exposure to estrogen and drive the underlying pathogenesis. The data presented in this thesis open up several further avenues for investigation, including a robust immunohistochemical assay that can be used in prospective clinical trials and a quantitative immunofluorescence double-staining methodology that can be applied to large clinical cohorts with documented clinical outcomes to help reveal the significance and relative contributions of the co-expressing AR/ER subpopulations to breast cancer pathogenesis and progression. AR expression needs to be investigated in suitable dynamic models of disease progression in order to establish exactly how different populations of cells within the tumour interact and change over time and in response to therapy. These data provide the starting point for these more advanced studies.