Recent genomic advances have identified a potential for germline risk markers to predict risk of treatmentrelated toxicity in children treated for cancer. Although advances in pediatric cancer treatment have led to remarkable increases in survival rates, long-term morbidity and early mortality risks underscore the need for new approaches that minimize late toxicities while maintaining disease control; using genetic markers to predict risk and change therapy represents a potential strategy to reduce adverse outcomes. Genetic risk assessment has created great excitement about the possibility of individualized therapy adapted for patients who carry increased or decreased risk. For example, research in cancer survivor cohorts have demonstrated a five-fold increased risk of congestive heart failure and a seven-fold increased risk of premature cardiac death. Clinical practice recommendations have recently been published for individualized adaptation of treatment and surveillance based upon genetic cardiac-risk profile in childhood cancer patients receiving anthracycline therapy. However, considerable uncertainty surrounds how successfully genetic information may translate into improved care and outcomes for children with cancer. Further, the rarity of childhood cancer and the long latency needed to observe late outcomes limit the feasibility of prospective trials to evaluate a precisionmedicine approach to care and follow-up. We propose to employ a decision-analytic modeling approach to determine how genetic testing may inform clinical care, both of initial cancer therapy and post-treatment care based on individual susceptibility for cardiotoxicity. We will develop a novel, flexible microsimulation model of the clinical course of childhood cancer to project the full spectrum of health outcomes relevant to the childhood cancer, including initial disease control and treatment-related late toxicities, and then incorporate genetic data to assess the impact upon these outcomes. This modeling framework will integrate data from multiple resources, including the Childhood Cancer Survivors Study (CCSS) to (1) project long-term outcomes for children diagnosed with cancer; (2) determine the clinical impact of utilizing genetic variant testing for cardiotoxicity in guiding cancer care; and (3) assess how consideration of genetic markers can improve followup cardiac screening recommendations for at-risk survivors. We aim to portray the scope and nature of the uncertainties that surround model parameters and their impact on modeled outcomes by employing bootstrapping methods, statistical methods to extrapolate cardiotoxicity risks, and rigorous approaches to uncertainty analysis. By uniquely leveraging the CCSS data to characterize the lifelong treatment-related cardiotoxicity risks, our proposed research will establish a novel analytic framework for evaluating the uncertainties and tradeoffs surrounding the use of genetic testing in pediatric oncology, and form the basis for understanding the impact of genetic risk testing for other toxicities.