pVACtools is a cancer immunotherapy tools suite consisting of the following tools:
A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a VCF file.
A cancer immunotherapy pipeline for identifying and prioritizing neoantigens from a FASTA file.
A tool for detecting neoantigens resulting from gene fusions.
A tool designed to aid specifically in the construction of DNA-based cancer vaccines.
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.
New in Release 3.0.4¶
This is a bugfix release. It fixes the following problem(s):
This fixes an issue introduced in the previous release where VCF entries with no VAF value would result in an error.
This release adds a new constraint to the vaf cutoff command line arguments to ensure that they are a fraction between 0 and 1.
This release also fixes an issue where the wrong binding filter class was being used when running pVACfuse with allele-specific binding cutoffs.
New in Version 3.0¶
This version adds the following features, outlined below. Please note that pVACtools 3.0 is not backwards-compatible and certain changes will break old workflows.
The pVACapi and pVACviz tools have been removed. They have been replaced by the pVACview tool.
The package namespace has been updated. The files will now be installed underneath a
pvactoolsdirectory in your python package installation path.
The aggregated report format has been updated. The headers have been updated for clarity. An additional column
Allele Exprhas been added, representing RNA expression * RNA VAF. For more information see all_epitopes.aggregated.tsv Report Columns.
pVACfuse no longer supports inputs from Integrate NEO. Only AGFusion inputs will be supported going forward.
The format of the pVACfuse all_epitopes and filtered reports has been updated to remove columns that aren’t applicable for the tool. Please see the documentation for the pVACfuse Prediction Algorithms Supporting Percentile Information for more information.
This release adds a new tool, pVACview. pVACview is an R Shiny application that allows for that visualization of the pVACseq aggregated report file to review, explore, and prioritize the different neoantigen candidates predicted by pVACseq.
The 3.0 release adds several improvements to the reference proteome similarity step:
Users can now run the reference proteome similarity step with a standalone Protein BLAST installation. To use a standalone BLASTp installation, provide the installation path using the
--blastp-pathparameter. The supported Protein BLAST databases are
refseq_protein. Please reference the Installing BLAST documentation for further instructions.
When running the reference proteome similarity step using the NCBI Protein BLAST API, users can now pick between the
Parallelization has been added to the reference proteome similarity step. When running this step as part of the pVACseq, pVACfuse, or pVACbind pipelines, the existing
--tparameter will also be used to set the number of parallel threads in this step.
This release adds standalone commands to run stability predictions, cleavage site predictions, and the reference proteome similarity step on the output of the pVACseq, pVACfuse, and pVACbind pipelines.
Previously, when running NetChop for cleavage site predictions, predictions were made for each epitope individually. However, these predictions will differ if additional flanking amino acids are provided and will be stable with 9 or more flanking amino acids. We updated this step to make predictions with 9 flanking amino acids around each epitope to generate stable predictions.
This release adds a
--speciesoption to the
valid_allelescommands to filter alleles on a species of interest.
This release adds a
--pass-onlyflag to the
pvacseq generate_protein_fastacommands to only process VCF entries that do not have a FILTER set.
This release adds a new parameter
--tumor-purity. This parameter indicates the fraction of tumor cells in the tumor sample and is used during aggregate report creation for a simple estimation whether variants are subclonal or clonal based on VAF.
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.
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.
The pVACtools source code is available in GitHub.
This project is licensed under BSD 3-Clause Clear License.