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Grant Details

Grant Number: 5R03CA259896-02 Interpret this number
Primary Investigator: Mandelblatt, Jeanne
Organization: Georgetown University
Project Title: A Simulation Modeling Study to Support Personalized Breast Cancer Prevention and Early Detection in High-Risk Women
Fiscal Year: 2022


Abstract: Personalized care is complex in this unprecedented era of discovery and ‘big data’. The proposed study focuses on the real-world choices facing over 100,000 US women each year who are at higher than average risk of developing breast cancer due to risk factors such as breast density and genetic predisposition. Women at high risk of developing breast cancer are eligible for various breast cancer prevention and early detection options. Current clinical guidelines recommend that these women are offered risk reducing medication, and supplemental imaging with magnetic resonance imaging (MRI) in addition to annual mammography. Each of these choices has a different profile of benefits and harms that will depend on individual risk factors. Annual mammography and MRI can detect tumors early, leading to early diagnosis and improved survival, but have harms related to false positives linked to breast density. Risk-reducing medications reduce the likelihood of developing breast cancer by nearly half, but these medications can induce menopausal symptoms based on age, and in a small percent of women, increase the risk of endometrial cancer or other conditions. Ultimately, a woman’s choice of intervention may depend on how she will weigh harms against benefits for these different options and outcomes given individual risk. To address these complexities, past studies have focused on either on single risk factors, risk prediction tools with selected factors, or screening strategies alone. We propose to use an extant Cancer Intervention and Surveillance Modeling Network (CISNET) simulation model to synthesize data on clinical risk factors and the impact of early detection with screening and primary prevention with risk-reducing medication to provide personalized data that will help identify women who are more likely to benefit from various interventions or combinations of interventions with the least harms. The aims are to: Aim 1: a) Provide data on the benefits (e.g. avoiding breast cancer; early detection and improved survival) and harms (side effects of risk-reducing drugs; false positives with screening) of various combinations of risk reducing medication and screening strategies personalized by individual 5-year risk of developing breast cancer, breast density, and preferences (utility weights) for different experiences; and b) Conduct sensitivity analysis to estimate the effects of uncertainty in model inputs or assumptions on results. Aim 2: Explore the impact of adding PRS to 5-year risk estimates to further personalize information on the balance of benefits and harms of various risk-reducing medication and screening strategies. Aim 3: Conduct key informant interviews with health care providers to guide the future use of model results to support shared decision making. The results of this study will provide novel data to guide personalized care for high-risk women. In future research, these data could be integrated into a conversation aid to facilitate shared decision making during clinical encounters. This study contributes to the National Cancer Institute’s mission to support advances in cancer prevention and control, and use ‘big data’ to enable the translation of research into clinical practice.


Reassessing the Benefits and Harms of Risk-Reducing Medication Considering the Persistent Risk of Breast Cancer Mortality in Estrogen Receptor-Positive Breast Cancer.
Authors: Jayasekera J. , Zhao A. , Schechter C. , Lowry K. , Yeh J.M. , Schwartz M.D. , O'Neill S. , Wernli K.J. , Stout N. , Mandelblatt J. , et al. .
Source: Journal of clinical oncology : official journal of the American Society of Clinical Oncology, 2023-02-01; 41(4), p. 859-870.
EPub date: 2022-12-01.
PMID: 36455167
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Using Protection Motivation Theory to Predict Intentions for Breast Cancer Risk Management: Intervention Mechanisms from a Randomized Controlled Trial.
Authors: Conley C.C. , Wernli K.J. , Knerr S. , Li T. , Leppig K. , Ehrlich K. , Farrell D. , Gao H. , Bowles E.J.A. , Graham A.L. , et al. .
Source: Journal of cancer education : the official journal of the American Association for Cancer Education, 2023 Feb; 38(1), p. 292-300.
EPub date: 2021-11-23.
PMID: 34813048
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A scoping review of interactive and personalized web-based clinical tools to support treatment decision making in breast cancer.
Authors: Zhao A. , Larbi M. , Miller K. , O'Neill S. , Jayasekera J. .
Source: Breast (Edinburgh, Scotland), 2022 Feb; 61, p. 43-57.
EPub date: 2021-12-06.
PMID: 34896693
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Reflecting on 20 years of breast cancer modeling in CISNET: Recommendations for future cancer systems modeling efforts.
Authors: Trentham-Dietz A. , Alagoz O. , Chapman C. , Huang X. , Jayasekera J. , van Ravesteyn N.T. , Lee S.J. , Schechter C.B. , Yeh J.M. , Plevritis S.K. , et al. .
Source: PLoS computational biology, 2021 Jun; 17(6), p. e1009020.
EPub date: 2021-06-17.
PMID: 34138842
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