pVACseq: How to Identify Neoantigen Candidates from WES Data

By Lociven · NeoantigenLab · July 2026

Figure 1. pVACseq filtering pipeline — typical solid tumor with WES input.

pVACseq Series · Part 1 of 2

You have WES data from a tumor and matched normal. You want a list of neoantigen candidates. Where do you start?

pVACseq is the most widely used open-source pipeline for neoantigen identification from WES data. It is maintained by the McDonnell Genome Institute at Washington University and is actively used in clinical trials including personalized vaccine studies. This post explains what pVACseq does at each step, what its output means, and how to interpret the ranked candidate list without a bioinformatics background.

What pVACseq does

pVACseq takes annotated somatic variants (from a VCF file) and patient HLA types, then outputs a ranked list of neoantigen peptide candidates. The pipeline runs five steps:

  1. Variant annotation: VEP (Variant Effect Predictor) annotates each variant with the amino acid change and surrounding protein context
  2. Peptide generation: For each nonsynonymous variant, all overlapping 8–11 mer peptides containing the mutation are generated
  3. MHC binding prediction: NetMHCpan (and optionally others) scores each peptide against the patient's HLA alleles
  4. Filtering: Peptides are filtered by binding score, expression (RNA-seq VAF if available), and optionally by proteasomal cleavage score
  5. Ranking: Candidates are ranked by a composite score incorporating binding affinity, expression, and VAF

Input requirements

To run pVACseq you need:

  • Somatic VCF: Called with a variant caller (Mutect2, Strelka2, or similar) against a matched normal. Must be annotated with VEP before input.
  • HLA types: Patient-specific HLA class I alleles (HLA-A, B, C). Can be typed from WES using tools like OptiType or HLA-HD (covered in the next post).
  • RNA-seq data (optional but recommended): Tumor RNA-seq allows pVACseq to filter out variants that are not expressed. Neoantigens from unexpressed genes are not presented regardless of binding affinity.

Reading the output

The main output file is a TSV table. Key columns to understand:

Column What it means Good value
Best MT Score Best IC50 (nM) of mutant peptide across all tested lengths < 500 nM
Best MT Percentile %Rank EL of best mutant peptide < 2%
Corresponding WT Score IC50 of the equivalent wild-type peptide > 1000 nM (WT should NOT bind)
Tumor DNA VAF Variant allele frequency in tumor DNA High VAF = clonal mutation
Gene Expression RNA expression level (TPM) if RNA-seq provided > 1 TPM
Allele HLA allele for which this peptide binds Match to patient's HLA type

The WT/MT score ratio: the most important filter

A neoantigen candidate is most interesting when the mutant peptide binds HLA strongly AND the wild-type equivalent does not. If both bind equally, the immune system likely already tolerates the wild-type epitope — a de novo T cell response against the mutant is less likely.

The fold-change (WT IC50 / MT IC50) is sometimes called the "differential agretopicity index." A ratio > 10 is generally considered favorable. pVACseq reports both scores; calculating the ratio manually is straightforward.

Common mistakes when interpreting pVACseq output

Trusting the rank without checking expression. A peptide with 0.1% rank from an unexpressed gene is worthless. Always cross-reference with RNA-seq if available.

Ignoring VAF. Low VAF variants may be subclonal — present in only a fraction of tumor cells. Vaccines targeting subclonal neoantigens face an evolutionary moving target; the tumor can eliminate the subclone under immune pressure. Prioritize clonal variants (VAF close to estimated tumor purity).

Running only one predictor. NetMHCpan is the default, but pVACseq supports running multiple algorithms simultaneously and filtering by consensus. Candidates that score well across multiple predictors are more reliable.

Not filtering indels carefully. Frameshift indels generate many candidate peptides from the same mutation (every downstream reading frame is novel). These tend to cluster in the output and can dominate rankings. They are immunogenic but also harder to synthesize as long peptides for experimental validation.

After pVACseq: experimental validation

pVACseq gives you candidates. Validation is a separate problem:

  • Peptide synthesis: Top candidates are synthesized as 9-mers or long peptides (15–25 mers for CD4 responses)
  • ELISpot / ICS: Patient PBMCs or TILs stimulated with peptide pools to detect IFN-γ secretion
  • Tetramer staining: pMHC tetramers for confirmed binders allow direct quantification of neoantigen-specific T cells
  • T cell expansion: Peptide-stimulated expansion to generate neoantigen-reactive T cell lines for functional assays

In practice, validation rates from computational prediction alone are around 20–40% — meaning 60–80% of top-ranked candidates from pVACseq do not produce a detectable T cell response. This is why the field is investing heavily in better immunogenicity predictors beyond MHC binding.

The next post covers HLA typing from WES data — a prerequisite for pVACseq that many wet-lab researchers overlook or outsource without understanding what the tools are doing.

Tags: pVACseq, neoantigen pipeline, WES, somatic mutation, NetMHCpan, HLA, neoantigen prediction, tumor immunology

Get the protocol

NeoantigenLab Protocol Pack — pVACseq Step-by-Step ($29)

Everything above, but as a runnable protocol: exact pVACseq commands (including the correct `-e1` flag syntax, not the deprecated one), HLA input formatting, and output interpretation — fact-checked against current tool docs.

Get the Protocol Pack →

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