Clinics are increasingly adopting gene-expression profiling to diagnose breast cancer subtype, providing an intrinsic, molecular portrait of the tumor. For example, the PAM50-based Prosigna test quantifies expression of 50 key genes to classify breast cancer subtype, and this method of classification has been demonstrated to be superior over traditional immunohistochemical methods that detect proteins, to predict risk of disease recurrence. However, these tests were largely developed and validated using breast cancer samples from postmenopausal women. Thus, the accuracy of such tests has not been explored in the context of the hormonal fluctuations in estrogen and progesterone that occur during the menstrual cycle in premenopausal women. Concordance between traditional methods of subtyping and the new tests in premenopausal women is likely to depend on the stage of the menstrual cycle at which the tissue sample is taken and the relative effect of hormones on expression of genes versus proteins. The lack of knowledge around the effect of fluctuating estrogen and progesterone on gene expression in breast cancer patients raises serious concerns for intrinsic subtyping in premenopausal women, which comprise about 25% of breast cancer diagnoses. Further research on the impact of the menstrual cycle on intrinsic breast cancer profiling is required if premenopausal women are to benefit from the new technology of intrinsic subtyping.

Background Transforming growth factor beta1 (TGFB1) is a multi-functional cytokine that regulates mammary gland development and cancer progression through endocrine, paracrine and autocrine mechanisms. TGFB1 also plays roles in tumour development and progression, and its increased expression is associated with an increased breast cancer risk. Macrophages are key target cells for TGFB1 action, also playing crucial roles in tumourigenesis. However, the precise role of TGFB-regulated macrophages in the mammary gland is unclear. This study investigated the effect of attenuated TGFB signalling in macrophages on mammary gland development and mammary cancer susceptibility in mice. Methods A transgenic mouse model was generated, wherein a dominant negative TGFB receptor is activated in macrophages, in turn attenuating the TGFB signalling pathway specifically in the macrophage population. The mammary glands were assessed for morphological changes through wholemount and H&E analysis, and the abundance and phenotype of macrophages were analysed through immunohistochemistry. Another cohort of mice received carcinogen 7,12-dimethylbenz(a)anthracene (DMBA), and tumour development was monitored weekly. Human non-neoplastic breast tissue was also immunohistochemically assessed for latent TGFB1 and macrophage marker CD68. Results Attenuation of TGFB signalling resulted in an increase in the percentage of alveolar epithelium in the mammary gland at dioestrus and an increase in macrophage abundance. The phenotype of macrophages was also altered, with inflammatory macrophage markers iNOS and CCR7 increased by 110% and 40%, respectively. A significant decrease in DMBA-induced mammary tumour incidence and prolonged tumour-free survival in mice with attenuated TGFB signalling were observed. In human non-neoplastic breast tissue, there was a significant inverse relationship between latent TGFB1 protein and CD68-positive macrophages. Conclusions TGFB acts on macrophage populations in the mammary gland to reduce their abundance and dampen the inflammatory phenotype. TGFB signalling in macrophages increases mammary cancer susceptibility potentially through suppression of immune surveillance activities of macrophages.

Background: The Oncotype DX 21-gene Recurrence Score is a genomic-based algorithm that guides adjuvant chemotherapy treatment decisions for women with early-stage, oestrogen receptor (ER)-positive breast cancer. However, there are age-related differences in chemotherapy benefit for women with intermediate Oncotype DX Recurrence Scores that are not well understood. Menstrual cycling in younger women is associated with hormonal fluctuations that might affect the expression of genomic predictive biomarkers and alter Recurrence Scores. Here, we use paired human breast cancer samples to demonstrate that the clinically employed Oncotype DX algorithm is critically affected by patient age. Methods: RNA was extracted from 25 pairs of formalin-fixed paraffin-embedded, invasive ER-positive breast cancer samples that had been collected approximately 2 weeks apart. A 21-gene signature analogous to the Oncotype DX platform was assessed through quantitative real-time PCR, and experimental recurrence scores were calculated using the Oncotype DX algorithm. Results: There was a significant inverse association between patient age and discordance in the recurrence score. For every 1-year decrease in age, discordance in recurrence scores between paired samples increased by 0.08 units (95% CI − 0.14, − 0.01; p = 0.017). Discordance in recurrence scores for women under the age of 50 was driven primarily by proliferation-and HER2-associated genes. Conclusion: The Oncotype DX 21-gene Recurrence Score algorithm is critically affected by patient age. These findings emphasise the need for the consideration of patient age, particularly for women younger than 50, in the development and application of genomic-based algorithms for breast cancer care.