Biologic and Computational Modeling of Mammographic Density and Stromal Patterning

Abstract

Abstract : Mammographic density serves as independent marker of short term breast cancer risk and a surrogate marker of response to a variety of prevention agents1-3. Although a majority of breast cancers are epithelial in origin, there is evidence that stromal content of the breast is an important predictor or mammographic density. There is increasing evidence that the stroma plays a role in breast cancer initiation4. However, currently we lack an understanding of how mammographic density is affected by the individual contribution of epithelial and stromal components and the biological potential of stromal and/or epithelial cells. The goals of this synergistic grant proposal are to develop computational and biological tools to investigate the relationship between mammographic density, stromal content of the breast, and the role of stromal/epithelial interactions in regulating proliferation, and ultimately, short-term breast cancer risk. To achieve these goals we bring together investigators with expertise in mathematical fractal pattern assessment, 3-D models of stromal/epithelial interactions, and clinical breast cancer risk assessment. Together we propose to correlate computational models of mammographic and stromal patterning with biological assays of stromal/epithelial proliferation, and clinical outcome leading to the construction of multi-disciplinary tools for the classification of breast cancer risk and response to prevention strategies.