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pVACtools v6: A comprehensive suite for neoantigen prediction, visualization, and therapy design
My H Hoang1, Susanna Kiwala1, Megan Richters1
1Division of Oncology, Department of Medicine, Washington University School of Medicine, St Louis, MO, USA.
Abstract:
With the rise of checkpoint blockade therapies and neoantigen-based vaccines reaching later-stage trials, there is a growing need for computational tools to identify and prioritize neoantigens. pVACtools, initially introduced in 20161, is an open-source informatic suite designed to support basic and translational neoantigen research. pVACtools assists prediction, prioritization, and visualization of neoantigens, as well as design of neoantigen-based therapies. We describe several major advances to pVACtools since the last update: (1) expanded neoantigen quality and safety assessment features, including support for peptide presentation scoring, immunogenicity prediction, anchor residue analysis, reference proteome similarity, percentile score calculation; (2) addition of pVACsplice, a new tool for predicting neoantigens from tumor-specific cis-splicing mutations; (3) addition of pVACbind, a flexible tool that supports noncanonical neoantigen sources; (4) improvement in neoantigen selection strategies; (5) a substantially improved pVACvector algorithm that achieves higher DNA/mRNA vector vaccine design success rates with shorter runtimes; (6) new utilities to support synthetic long peptide vaccine design; (7) extended prediction support for many non-human species; and (8) addition of pVACcompare, a tool to support comparison between two pVACseq results. Together, these updates reinforce pVACtools as the field's most comprehensive toolkit for neoantigen research, from basic discovery to the design and execution of personalized cancer vaccine clinical trials.
Insights
pVACtools is an updated bioinformatics suite for neoantigen research. It now offers enhanced features for predicting and prioritizing neoantigens, supporting the development of personalized cancer vaccines.
Area of Science:
- Computational biology
- Immunoinformatics
- Cancer immunology
Background:
- Checkpoint blockade therapies and neoantigen vaccines are advancing.
- Computational tools are crucial for identifying and prioritizing neoantigens.
- pVACtools is an established open-source suite for neoantigen research.
Purpose of the Study:
- To describe major updates to the pVACtools suite.
- To enhance neoantigen prediction, prioritization, and vaccine design capabilities.
- To reinforce pVACtools as a comprehensive toolkit for neoantigen research.
Main Methods:
- Expanded quality and safety assessment features for neoantigens.
- Introduced pVACsplice for splicing mutation neoantigens and pVACbind for noncanonical sources.
- Improved pVACvector algorithm for vaccine design and added pVACcompare for result comparison.
- Extended support for non-human species and synthetic long peptide vaccine design.
Main Results:
- Enhanced neoantigen assessment including peptide presentation, immunogenicity, and similarity analysis.
- New tools facilitate neoantigen discovery from splicing mutations and other sources.
- Improved vaccine vector design algorithms and comparison utilities.
- Expanded species support for broader research applications.
Conclusions:
- The updated pVACtools suite provides advanced capabilities for neoantigen discovery and prioritization.
- New features enhance the design and execution of personalized cancer vaccine clinical trials.
- pVACtools remains a leading comprehensive toolkit for neoantigen research from discovery to clinical application.