Terminal duct lobular units (TDLUs) are structures in the breast which involute with the completion of childbearing and physiological ageing. Women with less TDLU involution are more likely to develop breast cancer than those with more involution. Thus, TDLU involution may be utilized as a biomarker to predict invasive cancer risk. Manual assessment of TDLU involution is a cumbersome and subjective process. This makes it amenable for automated assessment by image analysis. In this study, we developed and evaluated an acini detection method as a first step towards automated assessment of TDLU involution using a dataset of histopathological whole-slide images (WSIs) from the Nurses’ Health Study (NHS) and NHSII. The NHS/NHSII is among the world's largest investigations of epidemiological risk factors for major chronic diseases in women. We compared three different approaches to detect acini in WSIs using the U-Net convolutional neural network architecture. The approaches differ in the target that is predicted by the network: circular mask labels, soft labels and distance maps. Our results showed that soft label targets lead to a better detection performance than the other methods. F₁ scores of 0.65, 0.73 and 0.66 were obtained with circular mask labels, soft labels and distance maps, respectively. Our acini detection method was furthermore validated by applying it to measure acini count per mm² of tissue area on an independent set of WSIs. This measure was found to be significantly negatively correlated with age.

Background: Modified median and subgroup-specific gene centering are two essential pre-processing methods to assign breast cancer molecular subtypes by PAM50. We evaluated the PAM50 subtypes derived from both methods in a subset of Nurses’ Health Study (NHS) and NHSII participants; correlated tumor subtypes by PAM50 with immunohistochemistry (IHC) surrogates; and characterized the PAM50 subtype distribution, proliferation scores and risk of relapse with proliferation and tumor size weighted (ROR-PT) scores in the NHS/NHSII. Methods: PAM50 subtypes, proliferation scores and ROR-PT scores were calculated for 882 invasive breast tumors and 695 histologically normal tumor-adjacent tissues. Cox proportional hazard models evaluated the relationship between PAM50 subtypes or ROR-PT scores/groups with recurrence free survival (RFS) or distant RFS. Results: PAM50 subtypes were highly comparable between the two methods. The agreement between tumor subtypes by PAM50 and IHC surrogates improved to fair when Luminal subtypes were grouped together. Using the modified median method, our study consisted of 46% Luminal A, 18% Luminal B, 14% HER2-enriched, 15% Basal-like and 8% Normal-like subtypes; 53% of tumor-adjacent tissues were Normal-like. Women with the Basal-like subtype had a higher rate of relapse within five years. HER2-enriched subtypes had poorer outcomes prior to 1999. Conclusions: Either pre-processing method may be utilized to derive PAM50 subtypes for future studies. The majority of NHS/NHSII tumor and tumor-adjacent tissues were classified as Luminal A and Normal-like, respectively. Impact: Pre-processing methods are important for the accurate assignment of PAM50 subtypes. These data provide evidence that either pre-processing method can be used in epidemiological studies.

Background: Hormone receptor signaling is critical in the progression of breast cancers, although the role of the androgen receptor (AR) remains unclear, particularly for estrogen receptor (ER)-negative tumors. This study assessed AR protein expression as a prognostic marker for breast cancer mortality. Methods: This study included 4147 pre- and postmenopausal women with invasive breast cancer from the Nurses' Health Study (diagnosed 1976-2008) and Nurses' Health Study II (1989-2008) cohorts. AR protein expression was evaluated by immunohistochemistry and scored through pathologist review and as a digitally quantified continuous measure. Hazard ratios (HR) and 95% confidence intervals (CI) of breast cancer mortality were estimated from Cox proportional hazards models, adjusting for patient, tumor, and treatment covariates. Results: Over a median 16.5 years of follow-up, there were 806 deaths due to breast cancer. In the 7 years following diagnosis, AR expression was associated with a 27% reduction in breast cancer mortality overall (multivariable HR = 0.73, 95% CI = 0.58 to 0.91) a 47% reduction for ER+ cancers (HR = 0.53, 95% CI = 0.41 to 0.69), and a 62% increase for ER- cancers (HR = 1.62, 95% CI = 1.18 to 2.22) (P heterogeneity < .001). A log-linear association was observed between AR expression and breast cancer mortality among ER- cancers (HR = 1.14, 95% CI = 1.02 to 1.26 per each 10% increase in AR), although no log-linear association was observed among ER+ cancers. Conclusions: AR expression was associated with improved prognosis in ER+ tumors and worse prognosis in ER- tumors in the first 5-10 years postdiagnosis. These findings support the continued evaluation of AR-targeted therapies for AR+/ER- breast cancers.

In post-menopausal women, high body mass index (BMI) is an established breast cancer risk factor and is associated with worse breast cancer prognosis. We assessed the associations between BMI and gene expression of both breast tumor and adjacent tissue in estrogen receptor-positive (ER+) and estrogen receptor-negative (ER−) diseases to help elucidate the mechanisms linking obesity with breast cancer biology in 519 post-menopausal women from the Nurses’ Health Study (NHS) and NHSII.