A Patient Centered Decision Support Framework for Breast Cancer

This project is a breast cancer specific realization of the project "Toward Genotype Phenotype Linkage to Optimize Cancer Treatments". How can emerging genetic and specific molecular information on breast cancer progression be linked to outcomes associated with different treatments, so that the individual needs of the patient physician interaction can be optimally coordinated? There are critical gaps between the collection of biomedical information (biological and medical informatics) and its most useful application for impacting selection of treatment options. Our goal is to provide an integrated informatics system focused on the key issues in breast cancer treatment that places decision making power in the hands of the clinician, while providing outcome probabilities for the options available to the patient. The DSF (decision support framework) design anticipates making optimum use of such emerging technologies as microarray measures of gene expression. The immediate goal is to demonstrate the feasibility of a DSF that will enable systematic individualization of breast tumor treatments. A computational database is being developed that represents a "mirror" population of breast cancer patients. The core experimental database includes over two dozen prognostic (i.e., breast cancer markers, such as ER status, Her2, etc.), risk, medical, and key patient factors for several hundred patients, plus results from recent pharmacogenetic clinical trials. Each new patient can be matched with a small set "surrogates" within the DSF database, which allows one to ask many "what if?" questions about treatment options and possible outcomes. The next step is to apply and develop algorithms to predict uncertainty throughout the decision making process. In part, our effort is similar to the application of Bayesian Networks to breast cancer. The project anticipates a future clinical setting where breast tumor treatments can be individually optimized, errors will diminish, outcomes will improve, and there will be considerable cost savings for the individual and for society.

Key personnel: Christine Case.

Collaborators: John W. Park, Laura J. Esserman, Laurence H. Baker, and Dan Moore


The premise of the agent paradigm, its related theory and methodologies together with advances in multilevel modeling of complex systems of interactions opened new frontiers for advancing the physical, natural, social, military, and information sciences and engineering...