Breast cancer (BC) is the second most common type of cancer and a major cause of death for women. Commonly, BC patients are assigned to risk groups based on the combination of prognostic and prediction factors (eg, patient age, tumor size, tumor grade, hormone receptor status, etc). Although this approach is able to identify risk groups with different prognosis, patients are highly heterogeneous in their response to treatments. To improve the prediction of BC patients, we extended clinical models (including prognostic and prediction factors with whole-omic data) to integrate omics profiles for gene expression and copy number variants (CNVs). We describe a modeling framework that is able to incorporate clinical risk factors, high-dimensional omics profiles, and interactions between omics and non-omic factors (eg, treatment). We used the proposed modeling framework and data from METABRIC (Molecular Taxonomy of Breast Cancer Consortium) to assess the impact on the accuracy of BC patient survival predictions when omics and omic-by-treatment interactions are being considered. Our analysis shows that omics and omic-by-treatment interactions explain a sizable fraction of the variance on survival time that is not explained by commonly used clinical covariates. The sizable interaction effects observed, together with the increase in prediction accuracy, suggest that whole-omic profiles could be used to improve prognosis prediction among BC patients.