Subregional Breast Density and Cancer Risk

描述（由申请人提供）：确定有患乳腺癌风险的妇女不是目前妇女健康临床范例的一部分，即使已经确定了诸如乳房密度等强烈的风险因素。乳房x光检查中高密度实质的比例高，似乎会使患乳腺癌的风险增加4-6倍。即使经过20年的研究，这种关联的生物学原因仍不清楚。这项拟议研究的长期目标是确定用于高死亡率癌症风险评估的最佳全球或局部乳腺密度测量方法。本应用程序的目的是描述乳腺体积密度和密度形态的特定局部测量与浸润性癌症和DCIS病例的癌症风险之间的关系，并发现血清、组织或临床生物标志物作为致密乳腺组织宏观分布的决定因素。中心假设是，分区域纤维腺体积密度百分比的测量与当地和全球乳腺癌风险的关系更为密切。我们对乳腺癌患者的乳腺组织中致密组织的分布情况知之甚少因为目前还没有关于致密组织分布的体内描述。我们的第二个假设是乳腺密度的特定生物标志物作为密度宏观结构的形态调节器。我们的方法是独特的，因为我们将使用一种新颖的逐像素测量乳房体积密度的方法，称为单x射线吸收测定法（SXA）。我们的第一个具体目标是确定在接受乳房x光检查的妇女中与随后的乳腺癌相关的致密乳腺体积亚区。这一目标的工作假设是，乳房的亚区域可能比整体乳房密度更能预测该区域局部癌症的风险。此外，由于排除了包围实质的外周脂肪，分区域密度可能比整体密度更能预测女性患乳腺癌的风险。我们的第二个具体目标是确定乳房形态与风险的关系，以及与关键组织、血清和临床相关的乳腺密度的形态稳定行为。已知密度的粗大分布显示了女性之间以及女性两个乳房之间的形态静止特性。我们的工作假设是，可能存在一种特定的空间分布，或致密组织的形态，与癌症风险相关，与密度的大小无关。我们的预期结果将包括：开发新的局部乳腺密度区域，这将用于我们的大型队列的未来研究，确认乳腺密度作为局部或全局风险因素，以及确定最可能驱动乳腺密度形态测定的生物标志物候选物。我们的研究结果将通过提供癌症风险标记物来智能地降低极低风险女性的筛查频率，从而降低接受乳房x光检查的女性的危害风险。这将有助于降低高风险妇女的死亡率，因为她们可以更准确地识别和确定使用更敏感的成像和减少风险战略的目标。

DESCRIPTION (provided by applicant): Identifying women at risk for breast cancer is not part of the current clinical paradigm for women's health even though strong risk factors, such as breast density, have been identified. A high percentage of dense parenchyma on mammograms appears to give a 4-6 fold risk to develop breast cancer. The biological cause for the association is unclear even after 20 years of study. The long-term goal of this proposed research is to determine the best global or local measure of breast density for risk assessment of high-mortality cancers. The objective of this application is to describe the relationship of specific local measures of volume breast density and density morphology to cancer risk for invasive cancers as well as DCIS cases, and to discover what serum, tissue, or clinical biomarkers act as a determinant of the macro distribution of dense breast tissue. The central hypothesis is that subregional measurements of the percent fibroglandular volume density are more strongly associated with local and global breast cancer risk. Very little is known of the distribution of dense tissue within breast tissue in women with cancer versus those without because an in vivo description of dense tissue distribution has not been available. Our secondary hypothesis is that specific biomarkers of breast density act as morphostats for density macro structure. Our approach is unique in that we will be using a novel pixel-by-pixel measure of volumetric breast density called Single X-ray Absorptiometry (SXA). Our first specific aim is to identify subregions of dense breast volume associated with subsequent breast cancer in women undergoing mammography. The working hypothesis for this aim is that subregions of the breast may be stronger risk predictor of local cancer in that region than global breast density. In addition, subregional density may be a stronger risk predictor of a woman's risk of breast cancer than global breast density due to the exclusion of peripheral adipose that envelopes the parenchyma. Our second specific aim is to Identify the association of breast morphology to risk and to key tissue, serum, and clinical correlates to test the morphostatic behavior of breast density. The coarse distribution of density is known to show morphostatic qualities between women as well as between a woman's two breasts. Our working hypothesis is that there may be a particular spatial distribution, or morphology, of dense tissue that is associated with cancer risk independent of the magnitude of the density. Our expected outcome will include: development of new regions of local breast density that will be made available for future studies using our large cohort, confirmation of breast density as either a local or global risk factor, and the identification of the most likely biomarker candidates driving breast density morphometry. Our findings will reduce the risks of harms for women undergoing mammography by providing a cancer risk marker to intelligently reduce the screening frequency of very low risk women. It will aid in the decreased mortality of high risk women by their more accurate identification and targeting for use of more sensitive imaging and risk reduction strategies. PUBLIC HEALTH RELEVANCE: The proposed research is relevant to the public health because it will attempt to significantly improve the ability to quantify a woman's personal risk of breast cancer using measures taken from her screening mammography images. This is relevant to the NIH's mission because improved measures of breast cancer risk will "advance significantly the Nation's capacity to protect and improve health."