Personalized medicine in oncology utilizes evidence derived from genetic, immune, and proteomic profiling to inform therapeutic options as well as provide prognostic information for each unique individual and their tumor. Our ability to biologically and immunologically define each patient's tumor has been driven by the development of assays characterizing the genomic and proteomic profiles of tumors that in turn have led to the development of large biologic databases and computational tools for the analysis of these large data sets. In Immuno-oncology, the introduction of checkpoint inhibitors and their approval across multiple tumor types has led to the recognition that the majority of patients will not clinically respond to these therapies but will remain at risk for the development of significant immunologic side effects. This challenge highlights the need for the development and validation of both predictive biomarkers for response to such therapies as well as biomarkers prognostic of disease course. Despite extensive investigation into predictive biomarkers using these biologic databases and computational methods, only recently has progress been made in this area. This progress is the first step allowing us to identify patients likely to benefit from these therapies and moving our field closer to a truly personalized approach to the use of immune therapies in oncology.
Keywords: Antibody; Immune monitoring; Immunologic biomarkers; Predictive biomarkers; Prognostic biomarkers; T-cell.