Grant Number:	1U01CA319080-01 Interpret this number
Primary Investigator:	Trentham-Dietz, Amy
Organization:	University Of Wisconsin-Madison
Project Title:	Collaborative Modeling to Identify Strategies to Improve Us Breast Cancer Outcomes in an Era of Innovation
Fiscal Year:	2026

ABSTRACT Breast cancer is a leading chronic disease. The rapid rate of new knowledge about molecular targets and acceleration in bioinformatics tools have led to important advances in breast cancer control strategies. The growing number of strategies is accompanied by uncertainty and challenges in translating results of trials into clinical practice. With five well-established models and a history of 284 highly cited publications informing public health and healthcare decisions, the Cancer Intervention and Surveillance Modeling Network (CISNET) Breast Working Group (BWG) is uniquely well-positioned to use comparative modeling to synthesize complex evidence to guide clinical translation and use of new cancer control strategies. Our Specific Aims are to: 1) Facilitate translation of risk-reducing and screening strategies into clinical practice for two distinct high-risk populations defined by inherited pathogenic gene variants and by elevated risk not attributable to genetic susceptibility; 2) Estimate the long-term population impact of artificial intelligence (AI) technologies for interpretation of screening mammograms; 3) Evaluate the benefits and toxicities of new and evolving cancer therapies to inform treatment decision-making and quantify the population impact on breast cancer mortality; and 4) Quantify the impact of post-diagnosis behavioral interventions on long-term patient-centered and survival outcomes among breast cancer survivors, focusing on exercise. Novel components of this cohesive proposal include quantification of side effects associated with risk-reducing interventions (including endometrial cancer); inclusion of AI as both a modeling method and the focus of scientific inquiry; interrogation of patient and tumor factors in treatment selection; and quantification of the impact of behavioral interventions. Each Aim includes 3-5 of the BWG models selected for their unique attributes. The models share common inputs and rigorous approaches for assessing validity and uncertainty of findings. The Aims encompass RFA priority areas 1, 3-8. We will leverage Rapid Response funds to address emerging advances and opportunities. We have integrated cross-cancer CISNET partnerships and structured within-BWG collaboration plans to foster the career development of early-stage investigators. The BWG will partner with advisors and multiple organizations including health decision experts, patient advocates, the Breast Cancer Surveillance Consortium, and clinical trial groups to ensure models address clinical needs and draw on rigorous and reproducible model input data. An experienced Coordinating Center will provide the infrastructure to support the project goals, including resource sharing, model accessibility, and assisting modeling teams with using AI-based computing approaches to accelerate calibration and runtime. The exceptional environments across the participating institutions provide valuable synergy and leveraging of resources to address new research questions that would not otherwise be possible. Overall, this research is aligned with NIH priorities to use next-generation tools such as AI and real-world data to test alternative strategies to improve chronic disease care via breast cancer risk reduction, early detection, treatment, and survivorship.





None