vaxrank

Vaxrank is the vaccine peptide ranking component of the OpenVax pipeline for designing personalized cancer vaccines. Given a patient's somatic mutations, tumor RNA sequencing data, and HLA type, Vaxrank selects and ranks the mutant peptides most likely to elicit a T-cell response, producing a report suitable for guiding vaccine manufacture.

Overview

Personalized cancer vaccines (also called neoantigen vaccines) work by training the immune system to recognise peptides that arise from somatic mutations unique to a patient's tumor. Designing such a vaccine requires a computational pipeline that bridges raw sequencing data and the peptide synthesiser:

1. Variant calling — Whole-exome or whole-genome sequencing of the tumor and matched normal identifies somatic mutations. This is typically done with tools such as MuTect or Strelka, upstream of Vaxrank.
2. Mutant transcript assembly — Tumor RNA-seq reads overlapping each mutation are assembled by Isovar to determine the true mutant protein sequence. This step phases nearby germline variants and captures any mutation-associated splicing differences, producing a more accurate reading frame than DNA-only prediction.
3. MHC binding prediction — Candidate epitopes (short peptide subsequences spanning the mutation) are scored for predicted binding to the patient's HLA class I molecules using mhctools, which wraps predictors such as MHCflurry, NetMHCpan, and BigMHC.
4. Vaccine peptide selection — Vaxrank assembles longer synthetic long peptides (SLPs, typically 25-mers) around the mutation, scores them by the number and strength of their predicted MHC-binding epitopes, filters out peptides that appear in the reference proteome, annotates known cancer hotspot mutations, and ranks candidates by a combined immunogenicity and manufacturability score.

Vaxrank outputs ranked reports in ASCII, HTML, PDF, and XLSX formats. Each report lists the top vaccine peptide candidates per variant, their predicted epitopes, and supporting evidence from the RNA data.

Clinical Use

Vaxrank is the ranking engine behind the OpenVax neoantigen vaccine pipeline, which has been used in several clinical trials of personalized cancer vaccines at Mount Sinai:

• PGV001 (NCT02721043) — A phase I study of personalised neoantigen vaccines in patients with solid and haematologic malignancies. All 11 treated patients developed neoantigen-specific T-cell responses (Bortman et al., Cancer Discovery 2025).
• PGV001 + atezolizumab in urothelial cancer (NCT03359239) — A phase I trial combining PGV001 with checkpoint inhibition. The combination was safe and induced neoantigen-specific CD4+ and CD8+ T-cell responses in all evaluated patients (Galsky et al., Nature Cancer 2025).
• PGV001 + TTFields in newly diagnosed glioblastoma (NCT03223103) — A phase I trial combining PGV001 with tumor treating fields and standard-of-care temozolomide (paper in preparation).

The computational pipeline used in these trials is described in Kodysh & Rubinsteyn, Methods Mol. Biol. 2020.

Quick Start

vaxrank \
    --vcf tests/data/b16.f10/b16.vcf \
    --bam tests/data/b16.f10/b16.combined.bam \
    --vaccine-peptide-length 25 \
    --mhc-predictor netmhc \
    --mhc-alleles H2-Kb,H2-Db \
    --padding-around-mutation 5 \
    --output-ascii-report vaccine-peptides.txt \
    --output-pdf-report vaccine-peptides.pdf \
    --output-html-report vaccine-peptides.html

Inputs:

• --vcf — Somatic variants (VCF from any variant caller)
• --bam — Tumor RNA-seq alignments (used by Isovar to assemble mutant transcripts)
• --mhc-alleles — Patient HLA alleles (e.g. HLA-A*02:01,HLA-B*07:02)
• --mhc-predictor — Which MHC binding predictor to use (see table below)

Installation

pip install vaxrank

Requirements: Python 3.9+

Vaxrank uses PyEnsembl for reference genome annotation. Install an Ensembl release matching your reference genome:

# GRCh38
pyensembl install --release 113 --species human
# GRCh37 (legacy)
pyensembl install --release 75 --species human

PDF report generation uses wkhtmltopdf by default:

brew install --cask wkhtmltopdf

Alternatively, pass --pdf-backend=weasyprint to use WeasyPrint (experimental), which has no external binary dependency:

pip install weasyprint
# macOS also needs: brew install pango

On Apple Silicon, WeasyPrint loads Pango via dyld, which doesn't search Homebrew's /opt/homebrew/lib by default. Add this to your shell profile:

export DYLD_FALLBACK_LIBRARY_PATH="/opt/homebrew/lib:$DYLD_FALLBACK_LIBRARY_PATH"

(Intel macOS doesn't need this — Homebrew's /usr/local/lib is in dyld's default fallback path.)

Configuration

YAML config file

Common parameters can be stored in a YAML file to avoid repeating them on every run:

vaxrank --config my_config.yaml --vcf variants.vcf --bam tumor.bam

Example my_config.yaml:

epitopes:
  min_score: 0.00001                        # drop epitopes below this score
  scoring_mode: affinity                    # "affinity" or "percentile_rank"
  logistic_midpoint: 350.0                  # IC50 (nM) at which score = 0.5
  logistic_width: 150.0                     # steepness of logistic curve
  affinity_cutoff: 5000.0                   # IC50 >= this → score 0
  percentile_rank_cutoff: 10.0              # rank >= this → score 0 (percentile mode)
  top_epitopes_per_candidate: 1000          # 0 = keep all

vaccine_peptides:
  preferred_length: 25                      # target amino acids per vaccine peptide
  min_length: 25                            # minimum vaccine peptide length
  max_length: 25                            # maximum vaccine peptide length
  padding_around_mutation: 5                # off-centre windows to consider
  per_mutation: 1                           # peptides to keep per variant
  max_epitopes_per_candidate: 1000          # 0 = keep all
  score_fraction_of_best: 0.99              # drop candidates scoring < 99% of best
  manufacturability:                        # GRAVY = mean hydropathy
    max_c_terminal_hydropathy: 1.5          # max GRAVY of C-terminal 7-mer
    min_kmer_hydropathy: 0.0                # min max-7mer GRAVY (floor)
    max_kmer_hydropathy_low_priority: 1.5   # low-priority max-7mer GRAVY cap
    max_kmer_hydropathy_high_priority: 2.5  # high-priority max-7mer GRAVY cap

Custom filtering and scoring with the topiary DSL

For anything beyond the scalar logistic / percentile-rank defaults, set epitopes.filter_expr and/or epitopes.score_expr to a topiary DSL string. Both accept the full topiary 5.0 expression grammar (kind accessors like affinity / presentation, arithmetic, & / |, .logistic(...) / .clip(...) transforms, column(col_name) for raw DataFrame columns, etc.).

epitopes:
  # Drop rows wholesale before scoring
  filter_expr: "affinity <= 500 & affinity.rank <= 2.0"
  # Compute a per-(peptide, allele) score in [0, 1] (binder-quality score)
  score_expr:  "affinity.logistic_normalized(350, 150)"

When filter_expr is omitted, no rows are dropped up-front; the default score_expr is synthesized from the scalar fields above (binding_affinity_cutoff, logistic_midpoint, logistic_width, etc.) and masked so ic50 >= affinity_cutoff → 0, reproducing the pre-5.0 behavior byte-for-byte.

Use affinity.logistic_normalized(m, w) for a [0, 1] binder-quality score (the topiary 5.1+ primitive); the plain affinity.logistic(m, w) is the raw sigmoid and caps below 1 (≈0.912 at default m=350, w=150).

Invalid DSL strings are rejected at config load (not mid-pipeline), so typos in the YAML surface before any predictions run.

CLI overrides

CLI arguments override YAML values. You can also use --config-value to override individual keys without editing the file:

vaxrank --config my_config.yaml \
  --config-value vaccine_peptides.score_fraction_of_best=0.95 \
  --config-value epitopes.percentile_rank_cutoff=5.0

Use --config-text when the right-hand side should be kept as a raw string instead of being YAML-parsed.

Resolution order

Config values are resolved in order (later wins):

1. Compiled-in defaults (see vaxrank/config/defaults.py)
2. YAML config file (--config)
3. --config-value / --config-text overrides
4. Dedicated CLI flags (e.g. --vaccine-peptide-length)

Config reference

EpitopeConfig — epitope scoring and filtering

Field	Default	Description
logistic_epitope_score_midpoint	350.0	IC50 (nM) at which epitope score = 0.5
logistic_epitope_score_width	150.0	Steepness of logistic scoring curve
min_epitope_score	0.00001	Epitopes scoring below this are dropped
binding_affinity_cutoff	5000.0	IC50 >= this → score 0
scoring_mode	"affinity"	"affinity" (IC50-based) or "percentile_rank"
percentile_rank_cutoff	10.0	Rank >= this → score 0 (percentile mode)
filter_expr	None	Topiary DSL string; drops rows where the expression is false. Parsed eagerly at config load.
score_expr	None	Topiary DSL string; overrides the default per-(peptide, allele) score.

VaccineConfig — peptide assembly and manufacturability

Field	Default	Description
preferred_peptide_length	25	Preferred amino acids per vaccine peptide
min_peptide_length	25	Minimum vaccine peptide length
max_peptide_length	25	Maximum vaccine peptide length
padding_around_mutation	5	Off-centre window positions to consider
max_vaccine_peptides_per_variant	1	Peptides to keep per variant
num_mutant_epitopes_to_keep	1000	Max epitope predictions per peptide (0 = all)
score_fraction_of_best	0.99	Drop candidates scoring below this fraction of the best
max_c_terminal_hydropathy	1.5	Max GRAVY score of the C-terminal 7-mer
min_kmer_hydropathy	0.0	Minimum max-7mer GRAVY (floor)
max_kmer_hydropathy_low_priority	1.5	Low-priority max-7mer GRAVY cap
max_kmer_hydropathy_high_priority	2.5	High-priority max-7mer GRAVY cap

The four *_hydropathy* fields control the manufacturability tie-breaking in vaccine peptide ranking. See VaccinePeptide.peptide_synthesis_difficulty_score_tuple for details on how each threshold is applied.

MHC Binding Predictors

Vaxrank integrates with MHC binding predictors via mhctools. Use --mhc-predictor <name> to select one:

--mhc-predictor	Tool	MHC Class	Notes
mhcflurry	MHCflurry	I	Open-source neural network; installed with mhctools
bigmhc	BigMHC	I	Auto-detects EL or IM model
bigmhc-el	BigMHC EL	I	Presentation (eluted ligand) model
bigmhc-im	BigMHC IM	I	Immunogenicity model
pepsickle	Pepsickle	I	Proteasomal cleavage predictor
netmhc	NetMHC	I	Auto-detects NetMHC3 or NetMHC4
netmhc3	NetMHC 3.x	I	Requires local install
netmhc4	NetMHC 4.0	I	Requires local install
netmhcpan	NetMHCpan	I	Auto-detects installed version
netmhcpan28	NetMHCpan 2.8	I	Requires local install
netmhcpan3	NetMHCpan 3.x	I	Requires local install
netmhcpan4	NetMHCpan 4.0	I	Default mode (EL + BA)
netmhcpan4-ba	NetMHCpan 4.0	I	Binding affinity mode only
netmhcpan4-el	NetMHCpan 4.0	I	Eluted ligand mode only
netmhcpan41	NetMHCpan 4.1	I	Default mode (EL + BA)
netmhcpan41-ba	NetMHCpan 4.1	I	Binding affinity mode only
netmhcpan41-el	NetMHCpan 4.1	I	Eluted ligand mode only
netmhcpan42	NetMHCpan 4.2	I	Default mode (EL + BA)
netmhcpan42-ba	NetMHCpan 4.2	I	Binding affinity mode only
netmhcpan42-el	NetMHCpan 4.2	I	Eluted ligand mode only
netmhccons	NetMHCcons	I	Requires local install
netmhcstabpan	NetMHCstabpan	I	Stability predictor; requires local install
netchop	NetChop	--	Proteasomal cleavage predictor
netmhciipan	NetMHCIIpan	II	Auto-detects installed version
netmhciipan3	NetMHCIIpan 3.x	II	Requires local install
netmhciipan4	NetMHCIIpan 4.0	II	Default mode (EL + BA)
netmhciipan4-ba	NetMHCIIpan 4.0	II	Binding affinity mode only
netmhciipan4-el	NetMHCIIpan 4.0	II	Eluted ligand mode only
netmhciipan43	NetMHCIIpan 4.3	II	Default mode (EL + BA)
netmhciipan43-ba	NetMHCIIpan 4.3	II	Binding affinity mode only
netmhciipan43-el	NetMHCIIpan 4.3	II	Eluted ligand mode only
mixmhcpred	MixMHCpred	I	Requires local install
netmhcpan-iedb	NetMHCpan via IEDB	I	Uses IEDB web API
netmhccons-iedb	NetMHCcons via IEDB	I	Uses IEDB web API
netmhciipan-iedb	NetMHCIIpan via IEDB	II	Uses IEDB web API
smm-iedb	SMM via IEDB	I	Uses IEDB web API
smm-pmbec-iedb	SMM-PMBEC via IEDB	I	Uses IEDB web API
random	Random	--	Returns random scores; for testing only

How It Works

Upstream inputs

Vaxrank does not perform variant calling or read alignment itself. Those steps happen upstream, typically as part of a larger bioinformatics pipeline (e.g. neoantigen-vaccine-pipeline):

1. Tumor and matched-normal DNA are sequenced and aligned; a variant caller (MuTect, Strelka, etc.) produces a VCF of somatic mutations.
2. Tumor RNA is sequenced and aligned to produce a BAM file.
3. The patient's HLA class I alleles are typed (from sequencing data or clinical records).

Vaxrank takes these three inputs — the VCF, the tumor RNA BAM, and the HLA alleles — and produces a ranked list of vaccine peptide candidates.

Mutant transcript assembly (Isovar)

For each somatic variant, Isovar extracts RNA-seq reads overlapping the mutant locus and assembles them into a mutant protein fragment. This is more accurate than simply applying the DNA variant to the reference transcript because it:

• Phases adjacent germline and somatic variants that fall on the same read, producing the true amino acid sequence
• Captures splicing differences such as intron retention events that may alter the reading frame near the mutation
• Confirms expression — variants with no supporting RNA reads are filtered out

Epitope scoring

Each mutant protein fragment is sliced into overlapping subsequences of epitope length (typically 8–15 amino acids). These candidate epitopes are scored for predicted MHC binding affinity using the selected predictor. Binding predictions are converted to a score between 0 and 1 via a logistic function parameterised by the EpitopeConfig settings.

Vaccine peptide ranking

Candidate vaccine peptides (longer SLPs, typically 25-mers) are constructed around each mutation. Each candidate is scored by the combined immunogenicity of the epitopes it contains. Candidates are then filtered and ranked by:

1. Epitope content — total predicted immunogenicity score
2. Reference proteome filtering — peptides matching the human reference proteome are removed to ensure only truly novel sequences are selected
3. Cancer hotspot annotation — variants at known recurrently mutated positions (bundled data from cancerhotspots.org, ~2,700 mutations across cancer types) are flagged
4. Manufacturability — tie-breaking by hydropathy-based synthesis difficulty (C-terminal and 7-mer window GRAVY scores)

Key modules

• core_logic.py: Main vaccine peptide selection algorithm
• epitope_logic.py: Epitope scoring and filtering
• reference_proteome.py: Set-based kmer index for reference proteome filtering (O(1) lookup, built once and cached)
• cancer_hotspots.py: Cancer mutation hotspot annotation
• vaccine_peptide.py: Vaccine peptide scoring and manufacturability
• report.py: Report generation (ASCII, HTML, PDF, XLSX)

Papers & 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:

Bortman 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), 930–945 (2025).

Galsky et al. Atezolizumab plus personalized neoantigen vaccination in urothelial cancer: a phase 1 trial. Nature Cancer (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}
}

Dependencies

Vaxrank is built on the OpenVax ecosystem:

• pyensembl: Reference genome annotation
• varcode: Variant effect prediction from DNA
• isovar: RNA-based mutant transcript assembly and variant phasing
• mhctools: Unified interface to MHC binding predictors

Other key dependencies:

• msgspec: Configuration serialization (YAML/JSON)
• pandas, numpy: Data processing
• jinja2, pdfkit/weasyprint: Report generation

Development

To install Vaxrank for local development:

git clone git@github.com:openvax/vaxrank.git
cd vaxrank
python -m venv venv
source venv/bin/activate
pip install -r requirements.txt
pip install -e .
# Examples; adjust release to match your reference
pyensembl install --release 113 --species human
pyensembl install --release 113 --species mouse

Run linting and tests:

./lint.sh && ./test.sh

The first run of the tests may take a while to build the reference proteome kmer index, but subsequent runs will use the cached index.

Scripts

• develop.sh: installs the package in editable mode and sets PYTHONPATH to the repo root.
• lint.sh: runs ruff on vaxrank and tests.
• test.sh: runs pytest with coverage.
• deploy.sh: runs lint/tests, builds a distribution with build, uploads via twine, and tags the release (vX.Y.Z). Deploy is restricted to the main/master branch.