pVACtools

pVACtools predicts and prioritizes tumor neoantigens by identifying altered peptides from somatic point mutations, in-frame and frameshift insertions/deletions, and gene fusions, and by evaluating peptide–MHC binding and molecular evidence to support personalized cancer vaccine design.

Key Features:

• End-to-End Neoantigen Characterization: Supports identification of altered peptides arising from somatic point mutations, in-frame and frameshift insertions/deletions, and gene fusions.
• MHC Binding Prediction Ensemble: Incorporates an ensemble of MHC Class I and II binding prediction algorithms and is extensible to integrate additional predictive algorithms.
• Peptide Prioritization: Prioritizes predicted peptides by integrating mutant allele expression levels, peptide–MHC binding affinities, and clonality (clonal versus subclonal mutations).
• Modular Workflow: Comprises modular components including pVACseq and pVACfuse for neoantigen prediction, pVACvector for DNA vector vaccine design and peptide ordering optimization to minimize junctional epitopes, and pVACviz for candidate prioritization.
• Synthetic Long Peptide Vaccine Support: Provides downstream analysis commands to evaluate candidates based on factors influencing peptide synthesis.
• Support for Diverse Vaccine Strategies: Supports design considerations specific to DNA vector-based vaccines and synthetic long peptide vaccines.

Scientific Applications:

• Neoantigen discovery and prioritization: Identification and ranking of candidate tumor neoantigens for personalized cancer immunotherapy.
• Vaccine construct design: Optimization of peptide ordering and assessment of junctional-epitope risk for DNA vector and synthetic long peptide vaccine constructs.
• Therapeutic candidate selection: Integration of expression and clonality data to select neoantigen candidates for downstream experimental validation and vaccine development.

Methodology:

Predicts neoantigens from somatic alterations using pVACseq and pVACfuse; evaluates peptide–MHC binding with an ensemble of MHC Class I and II algorithms; integrates mutant allele expression, peptide–MHC binding affinities, and clonality for prioritization; uses pVACvector to optimize peptide ordering to minimize junctional epitopes and provides downstream analyses for peptide synthesis considerations.