Personalized Medicine: Moving from Correlation to Causality in Breast Cancer

Personalized Medicine currently focuses on providing the right treatment to the right patient, but its long-term goal is provide appropriate healthcare, thus including personalized risk assessment and prevention. Much of this current emphasis results from access to technology advances in genetic testing and biomarker development and the desire to utilize their results to deliver enhanced patient care. Both approaches provide an overwhelming amount of data about a patient and most efforts to utilize this data for clinical decision support have relied on identifying and characterizing statistically significant patterns that correlate with risk of disease, disease diagnosis, prognosis, response to therapy, etc. Notable examples in breast cancer include the use of the Gail model for risk assessment, testing for BRCA mutations to optimize choices for prevention and the evaluation of expression levels of her2/neu to screen for potential responders to Herceptin. Although correlative indicators can enhance clinical decision making, the key to improving healthcare requires the difficult transition to understanding mechanisms of risk and disease. This requires the development and implementation of hybrid modeling approaches that address the underlying complexity of human physiology and the criticai dynamics present in ali biological processes. We have developed a fundamental model of the disease process, following a patient from pre-disease through early detection, treatment and outcome, and apply th?s multi-scalar approach across the risk assessment-enhanced diagnosis-therapeutic decision axis and will present the modeling methodologies used in each phase.-