Most of the 250,000 U.S. women diagnosed annually with in situ or invasive breast cancer (BC) were not recognized as being at increased risk for the disease. Both the NCI and Institute of Medicine have identified accurate individualized risk assessment as central to improving early detection and prevention. The most widely used risk prediction model for BC, the Gail model, generally performs well in predicting BC risk across groups of women, but is limited when predicting individualized risk. In general, the cancers for which we can predict susceptibility best are those where the at-risk tissue can be examined, e.g., cervix, colon, endometrium. In breast, benign tissue is available for risk assessment from women who have had a benign biopsy. Women with so-called benign breast disease (BBD), numbering 1-2 million/year In the US alone, are a common and clinically important group, with a known increased risk of BC. About 25% of women with BC report having had a prior benign biopsy. Our purpose with this project is to develop a risk prediction model for women with BBD. We hypothesize that features in benign breast tissue, in particular from subgroups known to be at increased risk for BC, can help to identify those women who are at highest likelihood of progression of BC. Two recognized higher-risk groups within BBD are women with atypia, and women in whom normal, age-related regression (or involution) of breast lobules has not occurred. Utilizing a cohort of over 11,000 women with BBD at the Mayo Clinic, we will identify novel BC-predictive biomarkers obtained from transcriptional profiling of women with atypia who either did or did not progress to BC and from dissection of the mechanisms underlying lobular involution, a physiological process which we recently found to be associated with decreased cancer risk. Using a newly-developed technique, we will also quantitate the specific extent of lobular involution that has occurred In these women to generate a continuous risk feature. Then in a nested case: control series within our BBD cohort, we will build a risk prediction model that incorporates the top predicting elements from histology, clinical-epidemiologic features, mammographic density, quantitation of involution and molecular biomarkers. Finally, building upon a strong collaboration fostered by Breast SPORE developmental funds, we will assess our BC risk model using the Nashville BBD cohort supported by the Vanderbilt Breast SPORE. Altogether these studies will test the ability to use tissue based features to enhance risk prediction of BC and potentially provide insights about important early events in breast carcinogenesis.

Public Health Relevance

With the proposed work, we are testing the principle that specific histopathologic and molecular features present in benign breast tissue can discriminate risk of a subsequent breast cancer more accurately than currently used clinical and epidemiologic features. Such a risk prediction approach would facilitate personalized clinical management strategies for the 1 -2 million US women/year who have a benign biopsy

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center (P50)
Project #
5P50CA116201-09
Application #
8757104
Study Section
Special Emphasis Panel (ZCA1-RPRB-7)
Project Start
Project End
Budget Start
2014-09-01
Budget End
2015-08-31
Support Year
9
Fiscal Year
2014
Total Cost
$442,263
Indirect Cost
$110,132
Name
Mayo Clinic, Rochester
Department
Type
DUNS #
006471700
City
Rochester
State
MN
Country
United States
Zip Code
55905
Abubakar, Mustapha; Orr, Nick; Daley, Frances et al. (2016) Prognostic value of automated KI67 scoring in breast cancer: a centralised evaluation of 8088 patients from 10 study groups. Breast Cancer Res 18:104
Cichon, Magdalena A; Moruzzi, Megan E; Shqau, Tiziana A et al. (2016) MYC Is a Crucial Mediator of TGFβ-Induced Invasion in Basal Breast Cancer. Cancer Res 76:3520-30
de la Hoya, Miguel; Soukarieh, Omar; López-Perolio, Irene et al. (2016) Combined genetic and splicing analysis of BRCA1 c.[594-2A>C; 641A>G] highlights the relevance of naturally occurring in-frame transcripts for developing disease gene variant classification algorithms. Hum Mol Genet 25:2256-2268
Durand, Nisha; Bastea, Ligia I; Long, Jason et al. (2016) Protein Kinase D1 regulates focal adhesion dynamics and cell adhesion through Phosphatidylinositol-4-phosphate 5-kinase type-l γ. Sci Rep 6:35963
Shi, Jiajun; Zhang, Yanfeng; Zheng, Wei et al. (2016) Fine-scale mapping of 8q24 locus identifies multiple independent risk variants for breast cancer. Int J Cancer 139:1303-17
Lei, Jieping; Rudolph, Anja; Moysich, Kirsten B et al. (2016) Genetic variation in the immunosuppression pathway genes and breast cancer susceptibility: a pooled analysis of 42,510 cases and 40,577 controls from the Breast Cancer Association Consortium. Hum Genet 135:137-54
(2016) Functional mechanisms underlying pleiotropic risk alleles at the 19p13.1 breast-ovarian cancer susceptibility locus. Nat Commun 7:12675
Li, Xu; Wang, Wenqi; Xi, Yuanxin et al. (2016) FOXR2 Interacts with MYC to Promote Its Transcriptional Activities and Tumorigenesis. Cell Rep 16:487-97
Schmidt, Marjanka K; Hogervorst, Frans; van Hien, Richard et al. (2016) Age- and Tumor Subtype-Specific Breast Cancer Risk Estimates for CHEK2*1100delC Carriers. J Clin Oncol 34:2750-60
Chiba, Akiko; Hoskin, Tanya L; Hallberg, Emily J et al. (2016) Impact that Timing of Genetic Mutation Diagnosis has on Surgical Decision Making and Outcome for BRCA1/BRCA2 Mutation Carriers with Breast Cancer. Ann Surg Oncol 23:3232-8

Showing the most recent 10 out of 381 publications