Vaxrank
Vaxrank selects mutated protein fragments for personalized therapeutic cancer vaccines. It is the antigen ranking and vaccine design component of the OpenVax pipeline.
It takes somatic variants, tumor RNA-seq and HLA types, or previously scored candidate epitopes, and returns ranked antigen windows, peptide or mRNA construct files, and reports for reviewing the evidence behind each design. Unified mutation/CTA/viral inputs and the expanded peptide/mRNA design workflows are experimental.
Install
pip install vaxrank
pyensembl install --release 113 --species human # match your reference genome
Vaxrank needs Python 3.10 or newer. PDF reports additionally need wkhtmltopdf or WeasyPrint; see installation.
Quick start
Choose the vaccine type first: peptide, mrna, or both. This smoke example
uses the mouse B16 melanoma files in a repository checkout and a random test
predictor. It exercises the workflow; its scores are not biological predictions.
vaxrank \
--vaccine-type peptide \
--download-reference-genome-data \
--vcf tests/data/b16.f10/b16.vcf \
--bam tests/data/b16.f10/b16.combined.bam \
--mhc-predictor random \
--mhc-alleles H2-Kb,H2-Db \
--output-ascii-report vaccine-peptides.txt \
--output-pdf-report vaccine-peptides.pdf \
--pdf-backend weasyprint
For a real VCF + RNA run, supply:
--vcf: somatic variants, from any variant caller--bam: tumor RNA-seq alignments, used by Isovar to assemble mutant transcripts--mhc-alleles: the patient's HLA alleles, e.g.HLA-A*02:01,HLA-B*07:02--mhc-predictor: which binding predictor to run (available predictors)
To get vaccine constructs rather than reports, add --output-dir and, for mRNA,
--vaccine-type mrna. Vaccine designs describes the
peptide and mRNA designs and the files each one writes.
Start from an already-scored report
When MHC prediction has happened upstream, design directly from a LENS or pVACseq report instead of a VCF and BAM:
vaxrank --input-lens patient.lens.tsv \
--vaccine-type mrna --output-dir mrna_out/ \
--ensembl-release 102
Use the Ensembl release the report was annotated with. Original predictions are reused by default. Input workflows covers pVACseq reports, saved Vaxrank evidence, combining several reports in one run, and when to compute new predictions instead.
Documentation
Full documentation: https://openvax.github.io/vaxrank/
- Installation: reference annotation and PDF backends.
- Input workflows: VCF/BAM, LENS, pVACseq, Topiary tables, CTA expression, antigen sequences and saved evidence.
- Vaccine designs: the design axes, peptide and mRNA construct files, linker library, manifest schema.
- Analysis outputs: report formats and the figure commands.
- Configuration: YAML config, override order, named policy bundles, RNA reconstruction context, Topiary scoring DSL.
- MHC binding predictors: supported predictor names.
- How Vaxrank works: pipeline stages, ranking criteria, data model, module map.
- Varcode, Isovar, and Vaxrank: which library owns which decision.
- Development: local setup, test data, release scripts.
Experimental workflows: unified antigen inputs, CTA expression inputs, mixed mutation/CTA/viral selection and window exposure.
Clinical studies
The OpenVax pipeline has used Vaxrank in these Mount Sinai trials:
- PGV001 / NCT02721043: personalized vaccines for solid and hematologic malignancies.
- NCT03359239: PGV001 with atezolizumab in urothelial cancer.
- NCT03223103: PGV001 with tumor treating fields and temozolomide in glioblastoma.
Methods and published results are cited below.
Papers and citations
Vaxrank algorithm:
Rubinsteyn, A., Hodes, I., Kodysh, J. & Hammerbacher, J. Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines. bioRxiv (2017).
OpenVax pipeline (methods):
Kodysh, J. & Rubinsteyn, A. OpenVax: An Open-Source Computational Pipeline for Cancer Neoantigen Prediction. Methods Mol. Biol. 2120, 147–160 (2020).
PGV001 clinical results:
Saxena, Marron, Kodysh, et al. PGV001, a Multi-Peptide Personalized Neoantigen Vaccine Platform: Phase I Study in Patients with Solid and Hematologic Malignancies in the Adjuvant Setting. Cancer Discovery 15(5), 930–947 (2025).
Saxena, Anker, Kodysh, et al. Atezolizumab plus personalized neoantigen vaccination in urothelial cancer: a phase 1 trial. Nature Cancer 6(6), 988–999 (2025).
BibTeX for the Vaxrank paper:
@article {Rubinsteyn142919,
author = {Rubinsteyn, Alex and Hodes, Isaac and Kodysh, Julia and Hammerbacher, Jeffrey},
title = {Vaxrank: A Computational Tool For Designing Personalized Cancer Vaccines},
year = {2017},
doi = {10.1101/142919},
publisher = {Cold Spring Harbor Laboratory},
URL = {https://www.biorxiv.org/content/early/2017/05/27/142919},
journal = {bioRxiv}
}
License
Apache 2.0. Contributions are welcome; see CONTRIBUTING.md and the code of conduct.
Release files for vaxrank 3.44.3
For a detailed explanation of source distributions (sdists) and built distributions (wheels), please see the package formats documentation.
Source distribution (sdist)
| File | Size | Uploaded | |
|---|---|---|---|
| vaxrank-3.44.3.tar.gz | 2.5 MB | Details |
Built distribution (wheel)
| File | Interpreter | ABI | Platform | Reset |
|---|---|---|---|---|
| vaxrank-3.44.3-py3-none-any.whl | Python 3 | none | any | Details |
Total release size: 5.1 MB
Release files / vaxrank-3.44.3.tar.gz
| Download URL | vaxrank-3.44.3.tar.gz |
|---|---|
| Size | 2.5 MB |
| Tags | Source |
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| Download URL | vaxrank-3.44.3-py3-none-any.whl |
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