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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

  1. HLA Typing - Determines patient HLA alleles from RNA-seq (if not provided)
  2. VCF Annotation - Adds protein consequences with VEP
  3. Binding Prediction - Predicts peptide-MHC binding with multiple algorithms
  4. Neoantigen Calling - Identifies tumor-specific peptides with pVACseq
  5. Immunogenicity Scoring - Ranks candidates by binding, expression, VAF, and specificity
  6. 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