More than one million women are diagnosed with benign breast disease (BBD) by percutaneous biopsy annually in the U.S. and would benefit from improved breast cancer (BC) risk information as they face screening and prevention decisions. BBD is associated with increases in BC risk, ranging from 1.5-2.0 times for least severe categories to fourfold for most severe types. However, these risks apply to groups of women, not individuals, and individual risk varies considerably within BBD categories. Further, we have shown that breast cancer (BC) risk prediction models, such as the ?Gail Model?, perform poorly among women with BBD. Previously, we developed the BBD-BC model for surgical biopsies, which provides individual risk estimates based on self-reported factors, detailed characteristics of BBD extent and severity, and assessment of involution (shrinkage and disappearance) of surrounding histologic structures (terminal duct lobular units (TDLUs)) from which most BC precursors arise. BBD-BC outperforms the Gail Model in predicting BC risk. However, given that radiologically-guided small (percutaneous) biopsies have largely replaced surgical biopsies for diagnosis, a new model based on this biopsy approach is needed. Further, the emergence of mammographic density as an important BC risk factor, development of novel methods to assess TDLU involution and increased use of biomarkers in routinely processed clinical samples offer an opportunity to develop an improved BC risk prediction tool for women with percutaneous biopsy diagnoses of BBD. The goal of this project is to build a BC risk prediction tool for women with BBD diagnosed on percutaneous needle biopsy that could be validated in diverse populations and implemented clinically. We propose to develop a cohort at Mayo that includes >7,000 women who were diagnosed with BBD on a percutaneous biopsy of whom >400 later developed BC. We will develop a model to predict BC that includes factors in the BBD-BC model for surgical biopsies. We will also assess mammographic density, measured as a volume and area, using validated methods. We will identify immunohistochemical markers that can be applied to BBD biopsies to predict future risk of developing BC and evaluate novel NanoString RNA assays, which measure expression of related genes as composite ?signatures? reflecting cancer-like characteristics, proliferation, and a mutation-like score for the important TP53 tumor suppressor gene. Finally, we will develop an epidemiologic ?case-cohort? that includes a random subset of women from the full cohort (n~500) and all the women that developed invasive BC (n~250). We will evaluate BC risk prediction in this case-cohort of 750 women to evaluate performance of risk models without biomarkers and with biomarkers using novel machine learning approaches that offer strengths compared with more typical statistical models. Using these data, we will build an absolute risk prediction model for the full cohort that can be tested in other populations.
More than one million women are diagnosed with benign breast disease (BBD) by percutaneous biopsy annually in the U.S. and would benefit from improved breast cancer (BC) risk information as they face screening and prevention decisions. Although routine pathology diagnoses stratify groups of women with BBD into different levels of BC risk, individual risks vary greatly within these categories. Leveraging discoveries in prior work that resulted in a predictive model for BBD diagnosed on surgical biopsies, we will build a new model for percutaneous biopsies (now the dominant sampling method), which incorporates mammographic density, novel tissue biomarkers and machine learning statistical approaches.
