PROJECT SUMMARY Accurate assessment of risk is a top priority in oncology due to the population burden of cancer. Breast cancer is the leading cancer diagnosis among women worldwide and accordingly has the longest and broadest focus on risk prediction. Most traditional prediction models only utilize baseline factors known to be associated with breast cancer risk. More recent models expand to place greater emphasis on genomic risk factors. However, the predominant move of adding genomic risk markers incorporates a measure that is invariant to time (based on SNPs) and do not necessarily solve the challenge of improving breast cancer risk classification. The intrinsic heterogeneity between and within patients over time are reflected in part, by the time-varying covariate trajectories, which may provide important information for the prediction of breast cancer risk. The accumulation of cancer risk over life, well documented for breast cancer, is ideally suited to methods that incorporate time- varying covariates. Theobjective of this proposal is toprovide novel statistical models that can incorporate patient heterogeneity in a personalized, dynamic manner leading to a more accurate risk prediction scheme. The proposed algorithms encompass innovative functional approaches to comprehensively characterize the changing pattern of the longitudinal trajectories by a set of outcome-independent/unsupervised and outcome- dependent/supervised features. The set of individual-specific features will contain information on the observed time-varying `pattern' rather than one-time exposure in existing methods, leading to a higher predictive power. The dynamic prediction models will be built in a stepwise fashion, starting with a single time-varying covariate, and extended to the multivariate settings, to accommodate multiple time-varying covariates. The proposed methods will be applied to the Nurses' Health Study and further assessed externally in the Mayo Mammography Health Study. All of the proposed methods will be accompanied with user-friendly open-source software.