Abstract P4-01-02: Identification of histopathologic determinants of mammographic breast density as a cancer risk factor
Authors: Keller BM , Batiste RC , Chen J , McDonald ES , Conant EF , Kontos D , Feldman MD 
Published: May 2016

Abstract
Background: Mammographic density and texture, the amount and appearance of fibroglandular tissue respectively, are known to be strong predictors of a woman's risk to develop breast cancer. However, how differences in underlying tissue biology are associated with the wide range of breast density patterns seen among women is currently unknown. Understanding the biological mechanisms driving breast density can be critical for developing targeted interventions aimed at reducing a woman's risk for breast cancer.
Methods: Digital mammograms and formalin-fixed paraffin-embedded biopsy tissue sections from 73 women who donated breast tissue to the Susan G. Komen for the Cure Tissue Bank at the Indiana University Simon Cancer Center were included in this analysis. Hematoxylin and eosin (H&E) and AE1/AE3 cytokeratin stains were applied to the tissue sections, which were then visually characterized by an expert breast pathologist. H&E stains were used to assess the presence (in ≥5% of the tissue section) or absence of non-proliferative fibrocystic changes, proliferative fibrocystic changes without atypia, inflammatory and reactive conditions, or benign tumors. AE1/AE3 stains were used to quantify the extent of epithelial and stromal content, as well as the number of lobules and ductal structures seen in the section. Quantitative breast percent density (PD%) and whole-breast texture analysis was performed on the digital mammograms using validated software. Parenchymal texture measures recently demonstrated to be associated with breast cancer risk were assessed, including the gray-level co-occurrence features Cluster-shade, Correlation, Energy, Entropy, Inertia and Inverse Difference Moment. Texture was characterized within a 2.5cm2 area of interest in the upper-outer quadrant of the mammograms corresponding to the approximate location of the biopsy. Partial-R2 (pR2) analysis was applied in order to assess the proportion of the variability in the mammographic texture which can be directly attributed to underlying histological properties after adjusting for race, menopausal status, body mass index, PD% and mammography unit manufacturer, utilizing a false-discovery-rate (FDR) multiple comparisons correction.
Results: The presence of proliferative fibrocystic changes without atypia was found to have a significant association to Correlation (pR2=0.21), Energy (pR2=0.13), Entropy (pR2=0.16), and Inverse Difference Moment (pR2=0.14) at the FDR-corrected significance level of p&lt;0.0042, even after adjusting for race, menopausal status, body mass index, PD% and the mammography unit manufacturer. These findings suggest that 13-21% of the variation in parenchymal texture patterns seen between women can be explained by the presence of proliferative fibrocystic changes without atypia, a common and well known risk factor for breast cancer.
Conclusion: In this study, we identified proliferative fibrocystic changes without atypia as one potential driver of image-based biomarkers of breast cancer risk. Ultimately, identification of radiographic biomarkers of tissue microenvironment could aid in the development of targeted chemopreventative interventions for women with dense breast tissue while simultaneously providing image-based biomarkers to monitor their efficacy.
Citation Format: Keller BM, Batiste RC, Chen J, McDonald ES, Conant EF, Kontos D, Feldman MD. Identification of histopathologic determinants of mammographic breast density as a cancer risk factor. [abstract]. In: Proceedings of the Thirty-Eighth Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2015 Dec 8-12; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2016;76(4 Suppl):Abstract nr P4-01-02.