Androgen Receptor Function and Targeted Therapeutics Across Breast Cancer Subtypes

Emily A. Kolyvas; Carlos Caldas; Kathleen Kelly; Saif S. Ahmad


Breast Cancer Res. 2022;24(79) 

In This Article


While AR continues to show promise as a biomarker and a therapeutic target in BC, its role in the modern-day management of patients remains uncertain. The previous sections illustrated that the prognostic value of AR may differ based on the clinical BC subtype (primarily based on PAM50 criteria). However, the precise association of AR positivity with prognosis remains unclear. One possible explanation for this may be that AR positivity, for instance as characterized by AR gene expression, does not accurately predict AR activity. Given the differences in biology and prognosis across BC subtypes, in order to accurately test the association between AR expression and activity, the method by which the BC subtypes are classified is of critical importance. To date, BC subtypes have primarily been characterized using PAM50 criteria which are molecularly stratified based on gene expression profiling and relate to oestrogen and progesterone hormone receptor status. While this classification system is useful, it has its limitations, namely substantial variation within groups and lack of representation of rarer subtypes.[117,118] Based on the understanding that much of the gene expression landscape is driven by copy number alterations (CNAs), a new classification system for BC was developed.[119]

The Molecular Taxonomy of Breast Cancer International Consortium (METABRIC, used a combination of gene expression and CNAs to stratify BC into 10 integrative clusters (IntClust/s). These IntClusts cover the subtypes defined using other approaches; however, they also include novel tumour subtypes.[120,121] The IntClusts represent distinct clinical characteristics and response to therapeutics, and one may hypothesize that assessing AR positivity across clusters may help to resolve some of the conflicting prognostic significance seen when stratifying by PAM50 criteria. While more detailed in silico analysis, followed by validation in preclinical and clinical data sets is required to fully evaluate AR in BC IntClusts, we propose that the IntClust classification system may provide additional value in understanding the biological and clinical significance of AR expression in BC. This deeper understanding will support the characterization of BC subtypes in which AR status is more prognostic of outcome and help identify subtypes where therapeutically targeting AR may be most effective. Recent work has given more clarity on the clinical role of AR in ER+ BC; however, uncertainties remain in the other disease subtypes. It can be argued that understanding AR as it relates to the genomic landscape of BC, as classified by integrative clusters, would be a more nuanced way of stratifying patients for AR therapy. Alternatively, biomarkers that capture active AR signalling driving tumorigenesis/tumour maintenance may be necessary to faithfully identify patients most likely to benefit from AR-directed therapy. However, identifying and validating these biomarkers is unlikely to be trivial and may require interrogation of both transcriptional regulation and gene expression through methods such as chromatin immunoprecipitation (ChIP) and RNA sequencing. Targeting AR has shown promising results in multiple clinical trials, along with androgen-targeting therapies in combination with other therapies, such as tamoxifen in ER+ BC or RT in TNBC. Harnessing the full potential of targeting AR will require a more refined understanding of the role of AR in each subtype of BC.