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Grant Details

Grant Number: 1U01CA253911-01 Interpret this number
Primary Investigator: Trentham-Dietz, Amy
Organization: University Of Wisconsin-Madison
Project Title: Comparative Modeling of Precision Breast Cancer Control Across the Translational Continuum
Fiscal Year: 2020


ABSTRACT The CISNET Breast Working Group (BWG) proposes innovative modeling research focused on new precision oncology paradigms that are expected to re-define breast cancer control best practices. We selected significant topics where modeling is suited to fill evidence gaps and facilitate clinical and policy translation. Unique components of our approach include modeling of absolute risk of disease accounting for multiple risk factors, addressing important comorbidities—specifically type 2 diabetes—that affect both disease risk and survival, exploring emerging biomarker-based approaches for screening, providing guidance regarding precision systemic treatments and their impact on quality of life in survivors, and investigating race disparities. The specific aims are to: 1) Evaluate the impact of novel precision screening approaches; 2) Evaluate the impact of precision treatment paradigms in the adjuvant, neo-adjuvant, and metastatic setting; 3) Synthesize Aims 1 and 2 to quantify the contributions of precision screening and precision treatment to US breast cancer mortality reductions; and 4) Provide evidence to guide interventions to reduce race disparities by quantifying multiple risk, screening, treatment, and survival factors that impact disparities. This scope of work would not be feasible without the availability of six distinctive BWG models: Dana Farber (D), Erasmus (E), Georgetown- Einstein (GE), MD Anderson (M), Stanford (S) and Wisconsin-Harvard (W). The aims encompass multiple RFA priority areas, and we have set aside Rapid Response funds to address remaining priority areas, support cross-cancer CISNET collaborations, and foster junior career enhancement. Each aim includes three or more model groups selected for their unique structure and includes outside collaborators and junior investigators. The models will share common inputs and provide a standard set of outcomes for benefits (e.g., distant recurrences and deaths avoided, mortality reductions, distant disease-free survival, and life years and quality- adjusted life years), harms (e.g., false positives and benign biopsies, interval cancers, advanced stage diagnoses, overdiagnosis and treatment impact on quality of life), and costs. Continuously funded for the past 19 years, the modeling teams have published 204 research papers informing public health policy decisions and trained 13 junior investigators. For this proposal, the BWG will partner with the American Cancer Society, the American College of Radiology, the American Society of Clinical Oncology, the Breast Cancer Surveillance Consortium, and others. An experienced Coordinating Center provides the infrastructure to support the project goals including resource sharing and model accessibility. The exceptional environment provides unprecedented synergy and leveraging of resources to address new research questions and support career development that would not otherwise be possible. Overall, this research will advance modeling research and guide breast cancer control policy.


Impact of the COVID-19 Pandemic on Breast Cancer Mortality in the US: Estimates From Collaborative Simulation Modeling.
Authors: Alagoz O. , Lowry K.P. , Kurian A.W. , Mandelblatt J.S. , Ergun M.A. , Huang H. , Lee S.J. , Schechter C.B. , Tosteson A.N.A. , Miglioretti D.L. , et al. .
Source: Journal of the National Cancer Institute, 2021-11-02; 113(11), p. 1484-1494.
PMID: 34258611
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Recent Changes in the Patterns of Breast Cancer as a Proportion of All Deaths According to Race and Ethnicity.
Authors: Trentham-Dietz A. , Chapman C.H. , Bird J. , Gangnon R.E. .
Source: Epidemiology (Cambridge, Mass.), 2021-11-01; 32(6), p. 904-913.
PMID: 34172689
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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 06; 17(6), p. e1009020.
EPub date: 2021-06-17.
PMID: 34138842
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