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Updated: Jul 23, 2025
Comparative Lesions Analysis Through a Targeted Sequencing Approach
Published on: November 5, 2019
Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors
Yang Shi1, Biyang Jing2, Ruibin Xi3,4
1School of Mathematical Sciences, Peking University, Beijing, China.
Background:
Neoantigens are critical for anti-tumor immunity and have been long-envisioned as promising therapeutic targets. However, current neoantigen analyses mostly focus on single nucleotide variations (SNVs) and indel mutations and seldom consider structural variations (SVs) that are also prevalent in cancer.
Results:
Here, we develop a computational method termed NeoSV, which incorporates SV annotation, protein fragmentation, and MHC binding prediction together, to predict SV-derived neoantigens. Analysis of 2528 whole genomes reveals that SVs significantly contribute to the neoantigen repertoire in both quantity and quality. Whereas most neoantigens are patient-specific, shared neoantigens are identified with high occurrence rates in breast, ovarian, and gastrointestinal cancers. We observe extensive immunoediting on SV-derived neoantigens, especially on clonal events, which suggests their immunogenic potential. We also demonstrate that genomic alteration-related neoantigen burden, which integrates SV-derived neoantigens, depicts the tumor-immune interplay better than tumor neoantigen burden and may improve patient selection for immunotherapy.
Conclusions:
Our study fills the gap in the current neoantigen repertoire and provides a valuable resource for cancer vaccine development.
Insights
Structural variations (SVs) significantly contribute to cancer neoantigens, offering new therapeutic targets. Our study introduces NeoSV, a method to identify these SV-derived neoantigens, improving cancer vaccine development and patient selection for immunotherapy.
Area of Science:
- Oncology
- Immunology
- Bioinformatics
Background:
- Neoantigens are crucial for anti-tumor immunity and cancer therapies.
- Current neoantigen analysis often overlooks structural variations (SVs), which are common in cancer genomes.
Purpose of the Study:
- To develop a computational method (NeoSV) for predicting neoantigens derived from structural variations (SVs).
- To investigate the contribution of SVs to the neoantigen repertoire and their implications for cancer immunotherapy.
Main Methods:
- Developed NeoSV, integrating SV annotation, protein fragmentation, and MHC binding prediction.
- Analyzed 2528 whole cancer genomes to identify SV-derived neoantigens.
- Assessed neoantigen characteristics, including patient-specificity, shared neoantigens, and immunoediting.
Main Results:
- SVs significantly expand the neoantigen repertoire in both quantity and quality.
- Identified shared neoantigens derived from SVs in breast, ovarian, and gastrointestinal cancers.
- Observed immunoediting on SV-derived neoantigens, indicating immunogenic potential.
- Neoantigen burden integrating SVs better reflects tumor-immune interactions and may aid immunotherapy patient selection.
Conclusions:
- The developed NeoSV method fills a critical gap in neoantigen identification by including SVs.
- This work provides a valuable resource for advancing cancer vaccine development and precision immunotherapy.
