# SVNeoPP: A Workflow for Structural-Variant-Derived Neoantigen Prediction and Prioritization Using Multi-Omics Data

## TL;DR

SVNeoPP is a new tool that identifies and prioritizes neoantigens from structural variants in tumors using multi-omics data, expanding the range of potential targets for cancer immunotherapy.

## Contribution

SVNeoPP introduces a reproducible workflow for predicting and prioritizing neoantigens derived from structural variants, integrating multi-dimensional evidence for improved accuracy.

## Key findings

- SVNeoPP successfully generated a high-confidence list of candidate neoantigens from hepatocellular carcinoma multi-omics data.
- Compared to other methods, SVNeoPP expanded the search space for SV-derived neoantigens and improved antigen-processing and HLA binding features.
- The tool integrates transcriptomic, proteomic, and immunogenicity data to prioritize neoantigens with interpretable evidence.

## Abstract

Structural variants (SVs) are widespread in tumors and can generate protein fragments with larger tumor–normal differences through genomic rearrangements or gene fusion events, making them an important source of neoantigens. In this study, we developed SVNeoPP (Structural Variant Neoantigen Prediction and Prioritization), a reproducible analytical workflow that converts SV-breakpoint information into peptide sequences, predicts candidate neoantigens, and prioritizes them through stepwise filtering supported by multi-dimensional evidence, including transcriptomic and proteomic data. In a proof-of-concept application to hepatocellular carcinoma (HCC) multi-omics datasets, SVNeoPP produced a high-confidence, high-priority shortlist of candidate neoantigens. SVNeoPP may be complemented as a useful bioinformatics tool for human genome structure and function analysis.

Background: Tumor neoantigens are key targets for personalized vaccines and T-cell therapies, yet most pipelines focus on neoantigens derived from SNV/small indel and often yield a limited number of high-quality candidates. SVs are prevalent in tumors and can generate novel chimeric sequences and neopeptides, making them a promising additional source of neoantigens. However, SV-derived neoantigen prediction remains challenging due to breakpoint uncertainty, isoform-dependent coding inference, and limited integration of multi-dimensional evidence and reproducibility. Methods: We developed SVNeoPP (Structural Variant Neoantigen Prediction and Prioritization), an end-to-end workflow for SV-derived neoantigen analysis. SVNeoPP takes WGS and RNA-seq as inputs, performs SV calling and annotation, and reconstructs altered transcripts and coding sequences in a traceable, isoform-aware manner to generate candidate peptides. Candidates are prescreened by integrating antigen-processing features with HLA binding prediction, and then hierarchically filtered and prioritized based on transcript expression, LC–MS/MS proteomics evidence, immunogenicity predictions, and sequence similarity to experimentally validated neoantigen databases. SVNeoPP is implemented in Snakemake to enable modular extension, checkpoint-based restarts, and end-to-end reproducibility. Results: Using a hepatocellular carcinoma (HCC) multi-omics dataset as a proof of concept, we demonstrated the performance of SVNeoPP and obtained a high-priority shortlist of candidate peptides. Compared with other methods, SVNeoPP substantially expanded the candidate search space for SV-derived neoantigens and showed more favorable distributions of antigen-processing and HLA binding features. Conclusions: SVNeoPP provides a reusable, traceable, and interpretable multi-dimensional evidence-driven framework for SV-derived neoantigens. As a complementary module to SNV/small-indel pipelines, it broadens the neoantigen candidate repertoire and generates ranked candidates with interpretable evidence to facilitate downstream prioritization and decision-making.