pVACtools

pVACtools is a cancer immunotherapy tools suite consisting of the following tools:

pVACseq

A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a VCF file.

pVACbind

A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a FASTA file.

pVACfuse

A tool for detecting neoantigens resulting from gene fusions.

pVACsplice

A tool for detecting neoantigens resulting from splice site variants.

pVACvector

A tool designed to aid specifically in the construction of DNA-based cancer vaccines.

pVACview

An application based on R Shiny that assists users in reviewing, exploring and prioritizing neoantigens from the results of pVACtools processes for personalized cancer vaccine design.

pVACtools immunotherapy workflow

Contents

New in Version 5.3.0

This is a minor feature release. It adds the following features:

It also fixes the following problem(s):

New in Version 5

This is a major version release. Please note that pVACtools 5.0 is not guaranteed to be backwards-compatible and certain changes could break old workflows.

New Tools

This release adds a new tool, pVACsplice, for prediction neoantigens from splice sites. Please see the full tool documentation for more information. by @mrichters in https://github.com/griffithlab/pVACtools/pull/911

New Features

  • This release refactors the pVACvector graph building algorithm in order to increase the probability for finding a solution and reduce the number of iterations needed before a solution is found. Please see the PR describtion for the full details. by @susannasiebert in https://github.com/griffithlab/pVACtools/pull/1163

  • Add a new --aggregate-inclusion-count-limit parameter to set the maximum number of epitopes to include in the metrics.json detailed data for variants that have a large number of candidate neoantigens (e.g., frameshifts). by @susannasiebert in https://github.com/griffithlab/pVACtools/pull/1147

  • Add a new --biotypes parameter which defines a list of transcript consequence biotypes that the predictions from pVACseq and pVACsplice should be limited to. by @mrichters in https://github.com/griffithlab/pVACtools/pull/911

  • Add support for additional alleles that weren’t previously supported, includings ones for dog, mouse, and MHC class II. by @susannasiebert in https://github.com/griffithlab/pVACtools/pull/1148

Bugfixes

Past release notes can be found on our Release Notes page.

To stay up-to-date on the latest pVACtools releases please join our Mailing List.

Citations

Jasreet Hundal , Susanna Kiwala , Joshua McMichael, Chris Miller, Huiming Xia, Alex Wollam, Conner Liu, Sidi Zhao, Yang-Yang Feng, Aaron Graubert, Amber Wollam, Jonas Neichin, Megan Neveau, Jason Walker, William Gillanders, Elaine Mardis, Obi Griffith, Malachi Griffith. pVACtools: A Computational Toolkit to Identify and Visualize Cancer Neoantigens. Cancer Immunology Research. 2020 Mar;8(3):409-420. doi: 10.1158/2326-6066.CIR-19-0401. PMID: 31907209.

Jasreet Hundal, Susanna Kiwala, Yang-Yang Feng, Connor J. Liu, Ramaswamy Govindan, William C. Chapman, Ravindra Uppaluri, S. Joshua Swamidass, Obi L. Griffith, Elaine R. Mardis, and Malachi Griffith. Accounting for proximal variants improves neoantigen prediction. Nature Genetics. 2018, DOI: 10.1038/s41588-018-0283-9. PMID: 30510237.

Jasreet Hundal, Beatriz M. Carreno, Allegra A. Petti, Gerald P. Linette, Obi L. Griffith, Elaine R. Mardis, and Malachi Griffith. pVACseq: A genome-guided in silico approach to identifying tumor neoantigens. Genome Medicine. 2016, 8:11, DOI: 10.1186/s13073-016-0264-5. PMID: 26825632.

Huiming Xia, My H. Hoang, Evelyn Schmidt, Susanna Kiwala, Joshua McMichael, Zachary L. Skidmore, Bryan Fisk, Jonathan J. Song, Jasreet Hundal, Thomas Mooney, Jason R. Walker, S. Peter Goedegebuure, Christopher A. Miller, William E. Gillanders, Obi L. Griffith, Malachi Griffith. pVACview: an interactive visualization tool for efficient neoantigen prioritization and selection. Genome Medicine. 2024, 16:132, DOI: 10.1186/s13073-024-01384-7. PMID: 39538339.

Source code

The pVACtools source code is available in GitHub.

License

This project is licensed under BSD 3-Clause Clear License.