Elevated rates of DCIS diagnoses are inherent to current breast cancer screening processes - almost 30% of screen-detected breast cancers are DCIS. Due to uncertainty in the natural history of DCIS, there is widespread concern regarding overtreatment. Unfortunately, it is currently impossible to determine which DCIS lesions are likely to progress to a potentially lethal invasive stage. Thus, current guidelines recommend relatively aggressive treatment for all women with DCIS, including surgery, radiation, and consideration of hormone therapy. To optimize the breast cancer screening process, there is an urgent need for identification of DCIS prognostic markers that would permit personalized treatment strategies. Mammographic breast density is a promising candidate as a prognostic marker to predict the likelihood of progression from DCIS to invasive disease. Currently, however, there Is only scarce data regarding the nature of the association between breast density and disease progression, and much uncertainty in our understanding of the biological mechanisms of breast density. Collagen is a major component of breast density and laboratory studies have shown that it plays a key role in facilitating tumor Invasion. The objective of our proposal is to translate these laboratory findings into advances in the development of breast density as a prognostic marker for DCIS.
We aim to 1) determine the association between mammographic breast density and disease-free survival among women with DCIS; 2) determine the association between collagen reorganization and disease-free survival among women with DCIS; and 3) assess whether the association between mammographic breast density and disease-free survival is mediated by collagen reorganization. To accomplish these aims, we will use data and tissue from the Vermont Breast Cancer Surveillance System, which includes linked patient risk factor, mammography, pathology, treatment, and cancer outcomes data for approximately 1,400 DCIS cases with up to 16 years of follow-up. We will use three different measures of breast density: the categorical BIF^DS assessment, a 2-D quantitative computer-assisted method (Cumulus), and a 3-D quantitative volumetric density assessment that permits measurement of breast density in specific regions of interest adjacent to the DCIS lesion. Multiphoton microscopy will be used to evaluate collagen reorganization in archived DCIS tumor specimens. This study will evaluate the potential for mammographic breast density and collagen reorganization to serve as potential markers for identifying DCIS cases that are not likely to progress or could be treated with only minimal intervention. This could lead to a substantial improvement in our ability to minimize the harms of breast cancer screening (overtreatment) while preserving the benefits (reductions in breast cancer morbidity and mortality).

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Specialized Center--Cooperative Agreements (U54)
Project #
5U54CA163303-02
Application #
8555418
Study Section
Special Emphasis Panel (ZCA1-SRLB-R (O1))
Project Start
2011-09-23
Project End
2016-05-31
Budget Start
2012-09-01
Budget End
2013-05-31
Support Year
2
Fiscal Year
2012
Total Cost
$103,253
Indirect Cost
$36,046
Name
University of Vermont & St Agric College
Department
Type
DUNS #
066811191
City
Burlington
State
VT
Country
United States
Zip Code
05405
Munoz, Diego F; Plevritis, Sylvia K (2018) Estimating Breast Cancer Survival by Molecular Subtype in the Absence of Screening and Adjuvant Treatment. Med Decis Making 38:32S-43S
Buist, Diana S M; Abraham, Linn; Lee, Christoph I et al. (2018) Breast Biopsy Intensity and Findings Following Breast Cancer Screening in Women With and Without a Personal History of Breast Cancer. JAMA Intern Med 178:458-468
Sprague, Brian L; Vacek, Pamela M; Herschorn, Sally D et al. (2018) Time-varying risks of second events following a DCIS diagnosis in the population-based Vermont DCIS cohort. Breast Cancer Res Treat :
Plevritis, Sylvia K; Munoz, Diego; Kurian, Allison W et al. (2018) Association of Screening and Treatment With Breast Cancer Mortality by Molecular Subtype in US Women, 2000-2012. JAMA 319:154-164
Trentham-Dietz, Amy; Ergun, Mehmet Ali; Alagoz, Oguzhan et al. (2018) Comparative effectiveness of incorporating a hypothetical DCIS prognostic marker into breast cancer screening. Breast Cancer Res Treat 168:229-239
Conklin, Matthew W; Gangnon, Ronald E; Sprague, Brian L et al. (2018) Collagen Alignment as a Predictor of Recurrence after Ductal Carcinoma In Situ. Cancer Epidemiol Biomarkers Prev 27:138-145
Lee, Janie M; Abraham, Linn; Lam, Diana L et al. (2018) Cumulative Risk Distribution for Interval Invasive Second Breast Cancers After Negative Surveillance Mammography. J Clin Oncol 36:2070-2077
van den Broek, Jeroen J; van Ravesteyn, Nicolien T; Mandelblatt, Jeanne S et al. (2018) Comparing CISNET Breast Cancer Models Using the Maximum Clinical Incidence Reduction Methodology. Med Decis Making 38:112S-125S
Mandelblatt, Jeanne S; Near, Aimee M; Miglioretti, Diana L et al. (2018) Common Model Inputs Used in CISNET Collaborative Breast Cancer Modeling. Med Decis Making 38:9S-23S
McCarthy, Anne Marie; Barlow, William E; Conant, Emily F et al. (2018) Breast Cancer With a Poor Prognosis Diagnosed After Screening Mammography With Negative Results. JAMA Oncol 4:998-1001

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