Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors
Abstract 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 a...
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| Format: | Article |
| Language: | English |
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BMC
2023-07-01
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| Series: | Genome Biology |
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| Online Access: | https://doi.org/10.1186/s13059-023-03005-9 |
| _version_ | 1797774175418449920 |
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| author | Yang Shi Biyang Jing Ruibin Xi |
| author_facet | Yang Shi Biyang Jing Ruibin Xi |
| author_sort | Yang Shi |
| collection | DOAJ |
| description | Abstract 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. |
| first_indexed | 2024-03-12T22:17:10Z |
| format | Article |
| id | doaj.art-b853ef7e03ba4267ae89db9c43a34f8d |
| institution | Directory Open Access Journal |
| issn | 1474-760X |
| language | English |
| last_indexed | 2024-03-12T22:17:10Z |
| publishDate | 2023-07-01 |
| publisher | BMC |
| record_format | Article |
| series | Genome Biology |
| spelling | doaj.art-b853ef7e03ba4267ae89db9c43a34f8d2023-07-23T11:15:56ZengBMCGenome Biology1474-760X2023-07-0124111710.1186/s13059-023-03005-9Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumorsYang Shi0Biyang Jing1Ruibin Xi2School of Mathematical Sciences, Peking UniversitySchool of Life Sciences, Peking UniversitySchool of Mathematical Sciences, Peking UniversityAbstract 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.https://doi.org/10.1186/s13059-023-03005-9NeoantigenStructural variationImmunotherapyBioinformaticsCancer vaccineTumor microenvironment |
| spellingShingle | Yang Shi Biyang Jing Ruibin Xi Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors Genome Biology Neoantigen Structural variation Immunotherapy Bioinformatics Cancer vaccine Tumor microenvironment |
| title | Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors |
| title_full | Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors |
| title_fullStr | Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors |
| title_full_unstemmed | Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors |
| title_short | Comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors |
| title_sort | comprehensive analysis of neoantigens derived from structural variation across whole genomes from 2528 tumors |
| topic | Neoantigen Structural variation Immunotherapy Bioinformatics Cancer vaccine Tumor microenvironment |
| url | https://doi.org/10.1186/s13059-023-03005-9 |
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