The CISNET Breast Working Group (BWG) proposes innovative modeling research focused on challenges in early detection and clinical management that are expected to re-define breast cancer control best practices.
The specific aims of this research are to: 1) evaluate the population impact of using polygenic risk to inform screening strategies; 2) assess use of emerging new imaging technologies for population screening; 3) evaluate use of active surveillance for the clinical management of screen-detected DCIS; 4) evaluate the impact of new molecular pathway- and genomic-targeted treatment paradigms in the adjuvant and recurrence settings; and 5) synthesize the methods to quantify the relative contributions of these new paradigms for screening and clinical management on US mortality trends for the general population and risk-stratified subpopulations.
These aims encompass four RFA priority areas, and we have set aside funds to address three additional emerging areas (international cancer control planning, cancer disparities, and cancer-specific priority areas [e.g, trials of local recurrence]). Based on unique features, the investigators will work in teams of 3-4 models to address these aims so that the topics can be evaluated efficiently within the project period. This scope of work would not be feasible without the availability of six distinctive BWG models. The BWG models include: Dana Farber (D), Erasmus (E), Georgetown-Einstein (GE), MD Anderson (M), Stanford (S) and Wisconsin-Harvard (W). These modeling teams have been continuously funded for the past 14 years. Their collaboration has been very productive, including publication of 162 research papers, and conduct of modeling to inform public health policy decisions. For this proposal, the BWG will partner with: the Breast Cancer Surveillance Consortium (BCSC); the American College of Radiology Imaging Network (ACRIN); the Genetic Associations and Mechanisms in Oncology (GAME-ON) researchers; the Canadian Partnership Against Cancer; the Evaluation of Genomic Applications in Practice and Prevention (EGAPP); the Athena DCIS registry; the Population-based Research Optimizing Screening though Personalized Regimens (PROSPR) program; the Cancer Research Network (CRN); and international groups. An experienced Coordinating Center provides the infrastructure to support the project goals. This exceedingly strong collaborative environment provides for unprecedented synergy and leveraging of resources to address new research questions that would not be possible outside of this setting. We will deploy novel statistical approaches to jointly estimate DCIS natural history parameters and calculate progression-free survival and survival post-progression. A unique component includes providing model results for use in an extant DCIS clinical management decision aid. Finally, we include formal training opportunities. Overall, this research will advance modeling research and continue to guide breast cancer control policy.

Public Health Relevance

Best practices for breast cancer control are being re-defined by rapidly evolving knowledge about common genetic risks, advances in early detection imaging techniques, evaluation of active surveillance of DCIS, and the dramatic evolution of targeted therapies for recurrent disease. However, some of these advances are diffusing into community practice ahead of evidence about their effects on population mortality. To inform policy and clinical decisions, we will use six breast cancer simulation models as a virtual laboratory to evaluate projected population mortality outcomes, harms, and costs of dissemination of these evolving cancer control paradigms.

National Institute of Health (NIH)
National Cancer Institute (NCI)
Research Project--Cooperative Agreements (U01)
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Special Emphasis Panel (ZCA1)
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Scott, Susan M
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Georgetown University
Internal Medicine/Medicine
Schools of Medicine
United States
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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
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