Toward Genotype Phenotype Linkage to Optimize Cancer Treatments

This project is part of a larger undertaking that will assemble, test and validate a prototype decision support framework (DSF). We are using both informatics and simulation strategies to create a "mirror" population of hypothetical patients that enables linkage of the information from highly multiplexed molecular analyses through available systems knowledge to patient clinical data so that expected changes in treatment outcomes for new patients can be visualized (on a computer) interactively as various options and/or new data are considered. The DSF is intended to function as part of an institution-wide, patient centered system. With such a system, the patient, the physician, and researchers will be able to detect important, decision impacting relationships contained within the cancer research and clinical trial databases relationships that otherwise might remain undetected. The DSF will also allow the decision makers, the patient and physician, to ask a variety of probing "what if" questions, and to visually compare and contrast the answers graphically represented, with expected treatment results in real time. Having such a capability will allow customized (and, ideally, optimized) individual treatments, thereby lowering costs while improving both outcomes and the patient's quality of life.

Project contact: C. Anthony Hunt

Collaborators: Oztech Systems, Management Sciences Associates, Dan H. Moore, Joe W. Gray

 
 

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...