Cancer Immunogenomics Approaches and Applications to Cancer Vaccines.

Abstract

The application of next-generation sequencing-based genomics and corresponding analytical pipelines have significantly improved our ability to identify tumor-unique antigenic peptides ("neoantigens") for the design of personalized vaccine therapies and to monitor immune responses to these vaccines. The more recent implementation of artificial intelligence and machine learning into several of the more complex analytical components of the neoantigen selection process has provided significant improvements across a number of previously difficult aspects within neoantigen identification, as we will describe. Related technologies and analytics have been developed that enable the characterization of changes to the tumor immune microenvironment facilitated by vaccination and monitor systemic responses in patients. Here, we review these new methods and their application to the design, implementation, and evaluation of cancer vaccines.