Mammographic density does not correlate with Ki-67 expression or cytomorphology in benign breast cells obtained by random periareolar fine needle aspiration from women at high risk for breast cancer

BACKGROUND:Ki-67 expression is a possible risk biomarker and is currently being used as a response biomarker in chemoprevention trials. Mammographic breast density is a risk biomarker and is also being used as a response biomarker. We previously showed that Ki-67 expression is higher in specimens of benign breast cells exhibiting cytologic atypia that are obtained by random periareolar fine needle aspiration (RPFNA). It is not known whether there is a correlation between mammographic density and Ki-67 expression in benign breast ductal cells obtained by RPFNA.METHODS:Included in the study were 344 women at high risk for developing breast cancer (based on personal or family history), seen at The University of Kansas Medical Center high-risk breast clinic, who underwent RPFNA with cytomorphology and Ki-67 assessment plus a mammogram. Mammographic breast density was assessed using the Cumulus program. Categorical variables were analyzed by ?2 test, and continuous variables were analyzed by nonparametric test and linear regression.RESULTS:Forty-seven per cent of women were premenopausal and 53% were postmenopausal. The median age was 48 years, median 5-year Gail Risk was 2.2%, and median Ki-67 was 1.9%. The median mammographic breast density was 37%. Ki-67 expression increased with cytologic abnormality (atypia versus no atypia; P = 0.001) and younger age (=50 years versus >50 years; P = 0.001). Mammographic density was higher in premenopausal women (P = 0.001), those with lower body mass index (P < 0.001), and those with lower 5-year Gail risk (P = 0.001). Mammographic density exhibited no correlation with Ki-67 expression or cytomorphology.CONCLUSION:Given the lack of correlation of mammographic breast density with either cytomorphology or Ki-67 expression in RPFNA specimens, mammographic density and Ki-67 expression should be considered as potentially complementary response biomarkers in breast cancer chemoprevention trials

Mammographic density, lobular involution, and risk of breast cancer

In this review, we propose that age-related changes in mammographic density and breast tissue involution are closely related phenomena, and consider their potential relevance to the aetiology of breast cancer. We propose that the reduction in mammographic density that occurs with increasing age, parity and menopause reflects the involution of breast tissue. We further propose that age-related changes in both mammographic density and breast tissue composition are observable and measurable phenomena that resemble Pike's theoretical construct of ‘breast tissue ageing'. Extensive mammographic density and delayed breast involution are both associated with an increased risk of breast cancer and are consistent with the hypothesis of the Pike model that cumulative exposure of breast tissue to hormones and growth factors that stimulate cell division, as well as the accumulation of genetic damage in breast cells, are major determinants of breast cancer incidence

Can the stroma provide the clue to the cellular basis for mammographic density?

Mammographic density is recognised as a useful phenotypic biomarker of breast cancer risk. Deeper understanding is needed of the cellular basis, but evidence is limited because of difficulty in designing studies to validate hypotheses. The ductal epithelial components do not adequately explain the physical and dynamic features observed. The stroma is thought to interact with ductal structures in cancer initiation. Stromal tissues might account for the mammographic features, and this interplay can be hypothesised to relate risk to density. In a paper in this issue of Breast Cancer Research, Alowami has shown a relationship between density and stromal proteins, which might provide useful insight into mammographic density

Mammographic density, breast cancer risk and risk prediction

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models

The spatial distribution of radiodense breast tissue: a longitudinal study

Introduction Mammographic breast density is one of the strongest known markers of susceptibility to breast cancer. To date research into density has relied on a single measure ( for example, percent density (PD)) summarising the average level of density for the whole breast, with no consideration of how the radiodense tissue may be distributed. This study aims to investigate the spatial distribution of density within the breast using 493 mammographic images from a sample of 165 premenopausal women (similar to 3 medio-lateral oblique views per woman).Methods Each breast image was divided into 48 regions and the PD for the whole breast ( overall PD) and for each one of its regions ( regional PD) was estimated. The spatial autocorrelation ( Moran's I value) of regional PD for each image was calculated to investigate spatial clustering of density, whether the degree of clustering varied between a woman's two breasts and whether it was affected by age and other known density correlates.Results The median Moran's / value for 165 women was 0.31 (interquartile range: 0.26, 0.37), indicating a clustered pattern. High-density areas tended to cluster in the central regions of the breast, regardless of the level of overall PD, but with considerable between-woman variability in regional PD. The degree of clustering was similar between a woman's two breasts (mean within-woman difference in Moran's / values between left and right breasts = 0.00 (95% confidence interval (CI) = -0.01, 0.01); P = 0.76) and did not change with aging (mean within-woman difference in I values between screens taken on average 8 years apart = 0.01 (95% CI = -0.01, 0.02); P = 0.30). Neither parity nor age at first birth affected the level of spatial autocorrelation of density, but increasing body mass index (BMI) was associated with a decrease in the degree of spatial clustering.Conclusions This study is the first to demonstrate that the distribution of radiodense tissue within the breast is spatially autocorrelated, generally with the high-density areas clustering in the central regions of the breast. The degree of clustering was similar within a woman's two breasts and between women, and was little affected by age or reproductive factors although it declined with increasing BMI

Breast cancer susceptibility loci and mammographic density

Introduction Recently, the Breast Cancer Association Consortium (BCAC) conducted a multi-stage genome-wide association study and identified 11 single nucleotide polymorphisms (SNPs) associated with breast cancer risk. Given the high degree of heritability of mammographic density and its strong association with breast cancer, it was hypothesised that breast cancer susceptibility loci may also be associated with breast density and provide insight into the biology of breast density and how it influences breast cancer risk. Methods We conducted an analysis in the Nurses\u27 Health Study (n = 1121) to assess the relation between 11 breast cancer susceptibility loci and mammographic density. At the time of their mammogram, 217 women were premenopausal and 904 women were postmenopausal. We used generalised linear models adjusted for covariates to determine the mean percentage of breast density according to genotype. Results Overall, no association between the 11 breast cancer susceptibility loci and mammographic density was seen. Among the premenopausal women, three SNPs (rs12443621 [TNRc9/LOC643714], rs3817198 [lymphocyte-specific protein-1] and rs4666451) were marginally associated with mammographic density (p \u3c 0.10). All three of these SNPs showed an association that was consistent with the direction in which these alleles influence breast cancer risk. The difference in mean percentage mammographic density comparing homozygous wildtypes to homozygous variants ranged from 6.3 to 8.0%. None of the 11 breast cancer loci were associated with postmenopausal breast density. Conclusion Overall, breast cancer susceptibility loci identified through a genome-wide association study do not appear to be associated with breast cancer risk

Experimental manipulation of radiographic density in mouse mammary gland

INTRODUCTION: Extensive mammographic density in women is associated with increased risk for breast cancer. Mouse models provide a powerful approach to the study of human diseases, but there is currently no model that is suited to the study of mammographic density. METHODS: We performed individual manipulations of the stromal, epithelial and matrix components of the mouse mammary gland and examined the alterations using in vivo and ex vivo radiology, whole mount staining and histology. RESULTS: Areas of density were generated that resembled densities in mammographic images of the human breast, and the nature of the imposed changes was confirmed at the cellular level. Furthermore, two genetic models, one deficient in epithelial structure (Pten conditional tissue specific knockout) and one with hyperplastic epithelium and mammary tumors (MMTV-PyMT), were used to examine radiographic density. CONCLUSION: Our data show the feasibility of altering and imaging mouse mammary gland radiographic density by experimental and genetic means, providing the first step toward modelling the biological processes that are responsible for mammographic density in the mouse