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SVNeoPP: A Workflow for Structural-Variant-Derived Neoantigen Prediction and Prioritization Using Multi-Omics Data
Wanyang An1,2, Xiaoxiu Tan2, Zhenhao Liu2
1School of Health Science and Engineering, University of Shanghai for Science and Technology, Shanghai 200093, China.
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.
Insights
This study introduces SVNeoPP, a novel computational workflow for identifying tumor neoantigens from structural variants (SVs). SVNeoPP expands the search for cancer neoantigens, improving personalized vaccine and T-cell therapy development.
Area of Science:
- * Oncology
- * Bioinformatics
- * Immunology
Background:
- * Tumor neoantigens are crucial for personalized cancer vaccines and T-cell therapies.
- * Current methods primarily focus on single nucleotide variants (SNVs) and small indels, yielding limited neoantigen candidates.
- * Structural variants (SVs) are prevalent in tumors and can generate novel neoantigens, but their prediction is challenging due to technical hurdles.
Purpose of the Study:
- * To develop an end-to-end computational workflow, SVNeoPP, for predicting and prioritizing neoantigens derived from structural variants (SVs).
- * To address challenges in SV-derived neoantigen prediction, including breakpoint uncertainty and isoform-dependent coding inference.
- * To integrate multi-dimensional evidence for robust neoantigen identification and facilitate downstream therapeutic applications.
Main Methods:
- * Developed SVNeoPP, a Snakemake-based workflow integrating whole genome sequencing (WGS) and RNA-sequencing (RNA-seq) data.
- * Implemented SV calling, annotation, and reconstruction of altered transcripts and coding sequences in an isoform-aware manner.
- * Employed hierarchical filtering and prioritization based on antigen-processing features, HLA binding, transcript expression, proteomics, immunogenicity, and similarity to validated neoantigen databases.
Main Results:
- * Demonstrated SVNeoPP's performance using a hepatocellular carcinoma (HCC) multi-omics dataset, generating a high-priority shortlist of candidate peptides.
- * Showcased SVNeoPP's ability to significantly expand the search space for SV-derived neoantigens compared to existing methods.
- * Observed more favorable antigen-processing and HLA binding feature distributions for SVNeoPP-identified candidates.
Conclusions:
- * SVNeoPP provides a reusable, traceable, and interpretable framework for multi-dimensional evidence-driven SV-derived neoantigen analysis.
- * This workflow complements SNV/small-indel pipelines, broadening the neoantigen repertoire.
- * Generates ranked neoantigen candidates with interpretable evidence to aid downstream prioritization and clinical decision-making.
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