Androgen receptor expression predicts beneficial tamoxifen response in oestrogen receptor-alpha-negative breast cancer

Background: Although the androgen receptor (AR) is frequently expressed in breast cancer, its relevance in the disease is not fully understood. In addition, the relevance of AR in determining tamoxifen treatment efficiency requires evaluation. Purpose: To investigate the tamoxifen predictive relevance of the AR protein expression in breast cancer. Methods Patients were randomised to tamoxifen 40 mg daily for 2 or 5 years or to no endocrine treatment. Mean follow-up was 15 years. Hazard ratios were calculated with recurrence-free survival as end point. Results: In patients with oestrogen receptor (ER)-negative tumours, expression of AR predicted decreased recurrence rate with tamoxifen (hazard ratio (HR) = 0.34; 95% confidence interval (CI) = 0.14-0.81; P = 0.015), whereas the opposite was seen in the AR- group (HR = 2.92; 95% CI = 1.16-7.31; P = 0.022). Interaction test was significant P &amp;lt; 0.001. Patients with triple-negative and AR+ tumours benefitted from tamoxifen treatment (HR = 0.12; 95% CI = 0.014-0.95 P = 0.044), whereas patients with AR- tumours had worse outcome when treated with tamoxifen (HR = 3.98; 95% CI = 1.32-12.03; P = 0.014). Interaction test was significant P = 0.003. Patients with ER+ tumours showed benefit from tamoxifen treatment regardless of AR expression. Conclusions: AR can predict tamoxifen treatment benefit in patients with ER- tumours and triple-negative breast cancer.Funding Agencies|Swedish research council [A0346701]; Swedish cancer foundation [13 0435]</p

Breast cancer heterogeneity in primary and metastatic disease

Breast cancer encompasses a heterogeneous collection of neoplasms with diverse morphologies, molecular phenotypes, responses to therapy, probabilities of relapse and overall survival. Traditional histopathological classification aims to categorise tumours into subgroups to inform clinical management decisions, but the diversity within these subgroups remains considerable. Application of massively parallel sequencing technologies in breast cancer research has revealed the true depth of variability in terms of the genetic, phenotypic, cellular and microenvironmental constitution of individual tumours, with the realisation that each tumour is exquisitely unique. This poses great challenges in predicting the development of drug resistance, and treating metastatic disease. Central to achieving fully personalised clinical management is translating new insights on breast cancer heterogeneity into the clinical setting, to evolve the taxonomy of breast cancer and improve risk stratification