Project Details
Description
Project Abstract
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.
Status | Finished |
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Effective start/end date | 7/5/18 → 6/30/23 |
Funding
- National Cancer Institute: $637,416.00
- National Cancer Institute: $639,236.00
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