Neoantigen Pipeline

Complete workflow from somatic variants to ranked neoantigen vaccine candidates for personalized cancer immunotherapy.

Workflow Overview

code

Somatic VCF (annotated) + Tumor RNA-seq (optional)
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[1. HLA Typing] --> arcasHLA / OptiType (if types not provided)
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[2. MHC Binding Prediction] --> MHCflurry / NetMHCpan
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[3. Neoantigen Calling] --> pVACseq
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[4. Immunogenicity Scoring] --> Multi-factor ranking
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Ranked Vaccine Candidates (TSV + visualizations)

Prerequisites

bash

pip install pvactools mhcflurry vatools

mhcflurry-downloads fetch

conda install -c bioconda vep arcashla optitype

Primary Path: pVACseq Pipeline

Step 1: HLA Typing (if not provided)

HLA types are critical for MHC binding prediction. If not already known from clinical testing:

bash

# From tumor RNA-seq BAM
arcasHLA extract tumor.bam -t 8 -o hla_output/
arcasHLA genotype hla_output/tumor.extracted.1.fq.gz hla_output/tumor.extracted.2.fq.gz \
    -g A,B,C,DRB1,DQB1,DPB1 -t 8 -o hla_output/

# Parse results
cat hla_output/tumor.genotype.json

python

import json

with open('hla_output/tumor.genotype.json') as f:
    hla_data = json.load(f)

hla_alleles = []
for gene, alleles in hla_data.items():
    for allele in alleles:
        hla_alleles.append(f'HLA-{allele}')

# Format for pVACseq: HLA-A*02:01,HLA-A*24:02,HLA-B*07:02,...
hla_string = ','.join(hla_alleles)
print(f'HLA alleles: {hla_string}')

Step 2: VCF Annotation with VEP

pVACseq requires VEP-annotated VCF with specific fields:

bash

# Annotate somatic VCF
vep --input_file somatic.vcf \
    --output_file somatic.vep.vcf \
    --format vcf --vcf --symbol --terms SO \
    --plugin Frameshift --plugin Wildtype \
    --offline --cache \
    --pick --fork 4

# Add expression data (optional but recommended)
vcf-expression-annotator somatic.vep.vcf \
    expression.tsv gene \
    -s tumor_sample \
    -o somatic.vep.expression.vcf

Step 3: Run pVACseq

bash

# Basic run with MHC Class I
pvacseq run \
    somatic.vep.vcf \
    tumor_sample \
    "HLA-A*02:01,HLA-A*24:02,HLA-B*07:02,HLA-B*44:02,HLA-C*07:02,HLA-C*05:01" \
    MHCflurry MHCnuggetsI NetMHCpan \
    pvacseq_output/ \
    -e1 8,9,10,11 \
    --iedb-install-directory /path/to/iedb \
    -t 8

# With expression filtering
pvacseq run \
    somatic.vep.expression.vcf \
    tumor_sample \
    "HLA-A*02:01,HLA-A*24:02,HLA-B*07:02,HLA-B*44:02" \
    MHCflurry NetMHCpan \
    pvacseq_output/ \
    -e1 8,9,10,11 \
    --tumor-purity 0.7 \
    --trna-vaf 0.1 \
    --expn-val 1 \
    -t 8

Step 4: Filter and Rank Candidates

python

import pandas as pd
import numpy as np

results = pd.read_csv('pvacseq_output/MHC_Class_I/tumor_sample.filtered.tsv', sep='\t')

# Binding affinity filter (IC50 <500nM considered strong binder)
# IC50 <500nM: strong binder; 500-5000nM: weak binder
strong_binders = results[results['Median MT IC50 Score'] < 500].copy()

# Differential agretopicity index (DAI): difference between MT and WT binding
# Higher DAI = more tumor-specific
strong_binders['DAI'] = strong_binders['Median WT IC50 Score'] - strong_binders['Median MT IC50 Score']

# Expression filter (if available)
if 'Gene Expression' in strong_binders.columns:
    # TPM >1 ensures detectable expression
    strong_binders = strong_binders[strong_binders['Gene Expression'] > 1]

# VAF filter: prioritize clonal mutations
# VAF >0.1 ensures mutation present in substantial tumor fraction
strong_binders = strong_binders[strong_binders['Tumor DNA VAF'] > 0.1]

# Multi-factor scoring
def immunogenicity_score(row):
    score = 0
    # Strong binding (IC50 <150nM is very strong)
    if row['Median MT IC50 Score'] < 150:
        score += 3
    elif row['Median MT IC50 Score'] < 500:
        score += 2

    # High DAI (tumor-specificity)
    if row['DAI'] > 1000:
        score += 2
    elif row['DAI'] > 500:
        score += 1

    # Clonal mutation (high VAF)
    if row['Tumor DNA VAF'] > 0.3:
        score += 2
    elif row['Tumor DNA VAF'] > 0.15:
        score += 1

    # Expressed (if available)
    if 'Gene Expression' in row.index and row['Gene Expression'] > 10:
        score += 1

    return score

strong_binders['Immunogenicity Score'] = strong_binders.apply(immunogenicity_score, axis=1)

# Rank by composite score
ranked = strong_binders.sort_values('Immunogenicity Score', ascending=False)

# Top candidates for vaccine
top_candidates = ranked.head(20)
top_candidates.to_csv('top_neoantigen_candidates.tsv', sep='\t', index=False)

print(f'Total strong binders: {len(strong_binders)}')
print(f'Top 20 candidates exported')
print(ranked[['Gene Name', 'MT Epitope Seq', 'HLA Allele', 'Median MT IC50 Score', 'DAI', 'Immunogenicity Score']].head(10))

Step 5: MHC Class II Neoantigens (CD4+ T cell help)

bash

pvacseq run \
    somatic.vep.vcf \
    tumor_sample \
    "DRB1*01:01,DRB1*07:01,DQB1*02:01,DQB1*03:01" \
    MHCnuggetsII NetMHCIIpan \
    pvacseq_class2_output/ \
    -e2 15 \
    --iedb-install-directory /path/to/iedb \
    -t 8

Alternative: Standalone MHCflurry

For quick binding predictions without full pVACseq pipeline:

python

from mhcflurry import Class1PresentationPredictor

predictor = Class1PresentationPredictor.load()

peptides = ['SIINFEKL', 'GILGFVFTL', 'NLVPMVATV']
alleles = ['HLA-A*02:01', 'HLA-B*07:02']

results = predictor.predict(peptides=peptides, alleles=alleles, verbose=0)
print(results[['peptide', 'allele', 'mhcflurry_presentation_score', 'mhcflurry_affinity']])

Visualization

python

import matplotlib.pyplot as plt
import seaborn as sns

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

# IC50 distribution
ax1 = axes[0]
ax1.hist(ranked['Median MT IC50 Score'], bins=50, edgecolor='black')
ax1.axvline(500, color='red', linestyle='--', label='500nM threshold')
ax1.set_xlabel('Median MT IC50 (nM)')
ax1.set_ylabel('Count')
ax1.set_title('Binding Affinity Distribution')
ax1.legend()

# DAI vs IC50
ax2 = axes[1]
scatter = ax2.scatter(ranked['Median MT IC50 Score'], ranked['DAI'],
                      c=ranked['Immunogenicity Score'], cmap='viridis', alpha=0.7)
ax2.set_xlabel('MT IC50 (nM)')
ax2.set_ylabel('Differential Agretopicity Index')
ax2.set_title('Tumor Specificity vs Binding')
plt.colorbar(scatter, ax=ax2, label='Immunogenicity Score')

# Top genes
ax3 = axes[2]
gene_counts = ranked['Gene Name'].value_counts().head(15)
gene_counts.plot(kind='barh', ax=ax3)
ax3.set_xlabel('Number of Neoantigens')
ax3.set_title('Top Genes with Neoantigens')

plt.tight_layout()
plt.savefig('neoantigen_summary.pdf')

Parameter Recommendations

Step	Parameter	Value	Rationale
pVACseq	-e1	8,9,10,11	MHC-I binds 8-11mer peptides
pVACseq	-e2	15	MHC-II binds 13-25mer, 15 is core
Filtering	IC50	<500nM	Standard strong binder threshold
Filtering	VAF	>0.1	Ensures clonal representation
Filtering	Expression	>1 TPM	Detectable transcription
Ranking	DAI	>500	Good tumor specificity

Troubleshooting

Issue	Likely Cause	Solution
No neoantigens found	Low mutation burden	Lower IC50 threshold to 1000nM
Missing HLA alleles	Incomplete typing	Use OptiType with WES data
VEP annotation errors	Plugin missing	Install Frameshift, Wildtype plugins
Expression data mismatch	Sample naming	Verify sample IDs match between VCF and expression
Low DAI values	Germline contamination	Ensure proper somatic filtering

Output Files

File	Description
`*.filtered.tsv`	pVACseq filtered neoantigens
`*.all_epitopes.tsv`	All predicted epitopes
`top_neoantigen_candidates.tsv`	Ranked vaccine candidates
`neoantigen_summary.pdf`	Visualization figures

Related Skills

•immunoinformatics/mhc-binding-prediction - MHCflurry parameters
•immunoinformatics/neoantigen-prediction - pVACtools details
•immunoinformatics/immunogenicity-scoring - Ranking algorithms
•immunoinformatics/epitope-prediction - B-cell epitopes
•clinical-databases/hla-typing - HLA determination methods
•workflows/somatic-variant-pipeline - Upstream somatic calling