tools-only Neoantigen Pipeline - Usage Guide
Overview
This workflow identifies tumor-specific neoantigens from somatic mutations for personalized cancer vaccine design. It integrates HLA typing, MHC binding prediction, and multi-factor immunogenicity scoring to rank vaccine candidates.
Prerequisites
pip install pvactools mhcflurry vatools pandas numpy matplotlib seaborn
mhcflurry-downloads fetch
conda install -c bioconda vep arcashla optitype samtools
Required databases:
- VEP cache for annotation
- IEDB tools (optional, for additional algorithms)
Quick Start
Tell your AI agent what you want to do:
- "Find neoantigens from my somatic VCF for vaccine design"
- "Predict MHC binding for tumor mutations"
- "Rank neoantigen candidates by immunogenicity"
- "Run the neoantigen pipeline with my HLA types"
Example Prompts
Complete pipeline
"Run neoantigen discovery on my tumor VCF with HLA-A02:01,HLA-B07:02"
"Find vaccine candidates from my annotated somatic mutations"
HLA typing
"Determine HLA types from my tumor RNA-seq BAM"
"Extract HLA alleles for neoantigen prediction"
Binding prediction
"Predict MHC Class I binding for my mutant peptides"
"Find strong binders (<500nM) in my neoantigen candidates"
Ranking
"Score neoantigens by immunogenicity and expression"
"Rank my neoantigen candidates for vaccine prioritization"
Input Requirements
| Input | Format | Description |
|---|---|---|
| Somatic VCF | VCF (VEP-annotated) | Tumor somatic mutations |
| HLA types | String | Comma-separated 4-digit HLA alleles |
| Expression (optional) | TSV | Gene-level TPM from tumor RNA-seq |
| Tumor BAM (optional) | BAM | For HLA typing if types unknown |
What the Agent Will Do
- HLA Typing - Determines patient HLA alleles from RNA-seq (if not provided)
- VCF Annotation - Adds protein consequences with VEP
- Binding Prediction - Predicts peptide-MHC binding with multiple algorithms
- Neoantigen Calling - Identifies tumor-specific peptides with pVACseq
- Immunogenicity Scoring - Ranks candidates by binding, expression, VAF, and specificity
- Visualization - Generates summary plots of candidate distribution
Key Parameters
| Parameter | Default | Description |
|---|---|---|
| IC50 threshold | 500 nM | Strong binder cutoff |
| Epitope lengths | 8,9,10,11 | MHC-I peptide lengths |
| VAF minimum | 0.1 | Variant allele frequency filter |
| Expression minimum | 1 TPM | Gene expression filter |
| DAI threshold | 500 | Differential agretopicity for specificity |
Tips
- Expression data improves ranking: Include tumor RNA-seq TPM when available
- Use multiple algorithms: MHCflurry + NetMHCpan gives more robust predictions
- Consider Class II: CD4+ T cell help improves vaccine efficacy
- Clonal mutations first: Prioritize high-VAF variants for broader tumor coverage
- Validate HLA typing: Clinical-grade HLA typing is more reliable than computational