Breast tissue composition and susceptibility to breast cancer

Abstract

Breast density, as assessed by mammography, reflects breast tissue composition. Breast epithelium and stroma attenuate x-rays more than fat and thus appear light on mammograms while fat appears dark. In this review, we provide an overview of selected areas of current knowledge about the relationship between breast density and susceptibility to breast cancer. We review the evidence that breast density is a risk factor for breast cancer, the histological and other risk factors that are associated with variations in breast density, and the biological plausibility of the associations with risk of breast cancer. We also discuss the potential for improved risk prediction that might be achieved by using alternative breast imaging methods, such as magnetic resonance or ultrasound. After adjustment for other risk factors, breast density is consistently associated with breast cancer risk, more strongly than most other risk factors for this disease, and extensive breast density may account for a substantial fraction of breast cancer. Breast density is associated with risk of all of the proliferative lesions that are thought to be precursors of breast cancer. Studies of twins have shown that breast density is a highly heritable quantitative trait. Associations between breast density and variations in breast histology, risk of proliferative breast lesions, and risk of breast cancer may be the result of exposures of breast tissue to both mitogens and mutagens. Characterization of breast density by mammography has several limitations, and the uses of breast density in risk prediction and breast cancer prevention may be improved by other methods of imaging, such as magnetic resonance or ultrasound tomography.

Figure 1

Mammographic density. Left, examples of variation in mammographic density: (A) 0%, (B) <10%, (C) <25%, (D) <50%, (E) <75%, (F) >75%. Right, (G) illustration of a computer-assisted measure. The outer (red) line shows the edge of the breast, the inner (green) line shows the edge of dense tissue. Percent density is calculated by dividing the dense area by the total area and multiplying by 100.

Figure 2

The Pike model. A) Pike model of mammary carcinogenesis. b = short-term increase in risk after FFTP; FFTP = first full-term pregnancy; LMP = last menstrual period; f0, f1, f2 are parameters of the model. B) Age-specific incidence of breast cancer observed and predicted by the Pike model. Reproduced with permission from Pike et al. (31).

Figure 3

Potential models of change in mammographic density with age. A–C) Three hypothetical models of ways in which mammographic density might change over the life span. All models show a reduction in the values for percent density (on the y-axis) with increasing age (on the x-axis) but are distinguished by differences in the change of the interquartile range (IQR) with age. This is shown by the 25th and 75th percentiles of the distribution of percent density. D) Observed distributions of percent breast water in mothers and daughters. The y-axis shows the percent of subjects, and the x-axis the value for percent breast water. Vertical arrows indicate median percent breast water values for each group. Reproduced with permission from Boyd et al. (40). IQR = interquartile range.

Figure 4

Breast tissue composition and mammographic density. Random biopsy samples were taken from breast tissue slices (A), and histological sections were prepared. The area of the tissue on the slide was outlined, and the randomly selected areas within each section were selected (B). Tissue sections stained with hematoxylin and eosin (C) and trichrome (F) were prepared, and assessed using thresholding software to determine the total areas of nuclei (D) and collagen (G) within each section. Associations between these measurements and percent mammographic density are shown in box plots: nuclear area (E) and collagen (H); both were statistically significant (P < .001). Modified and reproduced with permission from Li et al. (32).

Figure 5

Magnetic resonance of the breast. A and B) Examples of magnetic resonance tissue slices with green lines showing definition of breast outlines. Images show a breast with little water (A) and a breast with extensive water (B). C) Scatter plot of percent breast water by magnetic resonance imaging vs percent mammographic density (n = 100 subjects). Spearman correlation coefficient = .85 (P < .001). Reproduced with permission from Boyd et al. (40).

Figure 6

Ultrasound tomography of the breast. A) The ultrasound ring array surrounds the breast as it moves on a vertical trajectory from the chest wall to the nipple, acquiring data at discrete steps along the way. B) Each acquired dataset yields images of sound speed. C) Scatter plot of percent density by ultrasound tomography (USPD) vs percent mammographic density (n = 90 subjects). Spearman correlation coefficient = .75 (P < .001). D) Scatter plot of volumetrically averaged breast sound speed vs percent mammographic density (n = 92 subjects). Spearman correlation coefficient = .59 (P < .001). Reproduced with permission from Schuchmann et al. (125).