  Neoantigen targeting therapies including personalized vaccines have shown
promise in the treatment of cancers. Accurate identification/prioritization of
neoantigens is highly relevant to designing clinical trials, predicting
treatment response, and understanding mechanisms of resistance. With the advent
of massively parallel sequencing technologies, it is now possible to predict
neoantigens based on patient-specific variant information. However, numerous
factors must be considered when prioritizing neoantigens for use in
personalized therapies. Complexities such as alternative transcript
annotations, various binding, presentation and immunogenicity prediction
algorithms, and variable peptide lengths/registers all potentially impact the
neoantigen selection process. While computational tools generate numerous
algorithmic predictions for neoantigen characterization, results from these
pipelines are difficult to navigate and require extensive knowledge of the
underlying tools for accurate interpretation. Due to the intricate nature and
number of salient neoantigen features, presenting all relevant information to
facilitate candidate selection for downstream applications is a difficult
challenge that current tools fail to address. We have created pVACview, the
first interactive tool designed to aid in the prioritization and selection of
neoantigen candidates for personalized neoantigen therapies. pVACview has a
user-friendly and intuitive interface where users can upload, explore, select
and export their neoantigen candidates. The tool allows users to visualize
candidates using variant, transcript and peptide information. pVACview will
allow researchers to analyze and prioritize neoantigen candidates with greater
efficiency and accuracy in basic and translational settings. The application is
available as part of the pVACtools pipeline at pvactools.org and as an online
server at pvacview.org.
