Associations of reproductive breast cancer risk factors with breast tissue composition.

BACKGROUND: We investigated the associations of reproductive factors with the percentage of epithelium, stroma, and fat tissue in benign breast biopsy samples. METHODS: This study included 983 cancer-free women with biopsy-confirmed benign breast disease (BBD) within the Nurses' Health Study and Nurses' Health Study II cohorts. The percentage of each tissue type (epithelium, stroma, and fat) was measured on whole-section images with a deep-learning technique. All tissue measures were log-transformed in all the analyses to improve normality. The data on reproductive variables and other breast cancer risk factors were obtained from biennial questionnaires. Generalized linear regression was used to examine the associations of reproductive factors with the percentage of tissue types, while adjusting for known breast cancer risk factors. RESULTS: As compared to parous women, nulliparous women had a smaller percentage of epithelium (ß = - 0.26, 95% confidence interval [CI] - 0.41, - 0.11) and fat (ß = - 0.34, 95% CI - 0.54, - 0.13) and a greater percentage of stroma (ß = 0.04, 95% CI 0.01, 0.08). Among parous women, the number of children was inversely associated with the percentage of stroma (ß per child = - 0.01, 95% CI - 0.02, - 0.00). The duration of breastfeeding of ≥ 24 months was associated with a reduced proportion of fat (ß = - 0.30, 95% CI - 0.54, - 0.06; p-trend = 0.04). In a separate analysis restricted to premenopausal women, older age at first birth was associated with a greater proportion of epithelium and a smaller proportion of stroma. CONCLUSIONS: Our findings suggest that being nulliparous as well as having a fewer number of children (both positively associated with breast cancer risk) is associated with a smaller proportion of epithelium and a greater proportion of stroma, potentially suggesting the importance of epithelial-stromal interactions. Future studies are warranted to confirm our findings and to elucidate the underlying biological mechanisms.

Early-Life and Adult Adiposity, Adult Height, and Benign Breast Tissue Composition.

BACKGROUND: Early-life and adult anthropometrics are associated with breast density and breast cancer risk. However, little is known about whether these factors also influence breast tissue composition beyond what is captured by breast density among women with benign breast disease (BBD). METHODS: This analysis included 788 controls from a nested case-control study of breast cancer within the Nurses' Health Study BBD subcohorts. Body fatness at ages 5 and 10 years was recalled using a 9-level pictogram. Weight at age 18, current weight, and height were reported via questionnaires. A deep-learning image analysis was used to quantify the percentages of epithelial, fibrous stromal, and adipose tissue areas within BBD slides. We performed linear mixed models to estimate beta coefficients (ß) and 95% confidence intervals (CI) for the relationships between anthropometrics and the log-transformed percentages of individual tissue type, adjusting for confounders. RESULTS: Childhood body fatness (level ≥ 4.5 vs. 1), BMI at age 18 (≥23 vs. <19 kg/m2), and current adult BMI (≥30 vs. <21 kg/m2) were associated with higher proportions of adipose tissue [ß (95% CI) = 0.34 (0.03, 0.65), 0.19 (-0.04-0.42), 0.40 (0.12, 0.68), respectively] and lower proportions of fibrous stromal tissue [-0.05 (-0.10, 0.002), -0.03 (-0.07, 0.003), -0.12 (-0.16, -0.07), respectively] during adulthood (all P trend < 0.04). BMI at age 18 was also inversely associated with epithelial tissue (P trend = 0.03). Adult height was not associated with any of the individual tissue types. CONCLUSIONS: Our data suggest that body fatness has long-term impacts on breast tissue composition. IMPACT: This study contributes to our understanding of the link between body fatness and breast cancer risk.See related commentary by Oskar et al., p. 590.

Deep Learning Image Analysis of Benign Breast Disease to Identify Subsequent Risk of Breast Cancer.

Background: New biomarkers of risk may improve breast cancer (BC) risk prediction. We developed a computational pathology method to segment benign breast disease (BBD) whole slide images into epithelium, fibrous stroma, and fat. We applied our method to the BBD BC nested case-control study within the Nurses' Health Studies to assess whether computer-derived tissue composition or a morphometric signature was associated with subsequent risk of BC. Methods: Tissue segmentation and nuclei detection deep-learning networks were established and applied to 3795 whole slide images from 293 cases who developed BC and 1132 controls who did not. Percentages of each tissue region were calculated, and 615 morphometric features were extracted. Elastic net regression was used to create a BC morphometric signature. Associations between BC risk factors and age-adjusted tissue composition among controls were assessed using analysis of covariance. Unconditional logistic regression, adjusting for the matching factors, BBD histological subtypes, parity, menopausal status, and body mass index evaluated the relationship between tissue composition and BC risk. All statistical tests were 2-sided. Results: Among controls, direction of associations between BBD subtypes, parity, and number of births with breast composition varied by tissue region; select regions were associated with childhood body size, body mass index, age of menarche, and menopausal status (all P < .05). A higher proportion of epithelial tissue was associated with increased BC risk (odds ratio = 1.39, 95% confidence interval = 0.91 to 2.14, for highest vs lowest quartiles, P trend = .047). No morphometric signature was associated with BC. Conclusions: The amount of epithelial tissue may be incorporated into risk assessment models to improve BC risk prediction.