dbPepNeo: a manually curated database for human tumor neoantigen peptides

被引:36
|
作者
Tan, Xiaoxiu [1 ,2 ]
Li, Daixi [1 ]
Huang, Pengjie [1 ,2 ]
Jian, Xingxing [2 ,3 ,4 ]
Wan, Huihui [1 ,2 ]
Wang, Guangzhi [2 ,5 ]
Li, Yuyu [2 ,5 ]
Ouyang, Jian [2 ]
Lin, Yong [1 ]
Xie, Lu [2 ]
机构
[1] Univ Shanghai Sci & Technol, Sch Med Instrument & Food Engn, 516 Jungong Rd, Shanghai 200093, Peoples R China
[2] Shanghai Acad Sci & Technol, Shanghai Ctr Bioinformat Technol, 1278 Keyuan Rd, Shanghai 201203, Peoples R China
[3] Cent South Univ, Minist Educ, Key Lab Carcinogenesis & Canc Invas, 932 South Lushan Rd, Changsha 410083, Peoples R China
[4] Cent South Univ, Xiangya Hosp, Natl Hlth & Family Planning Commiss, Key Lab Carcinogenesis, 932 South Lushan Rd, Changsha 410083, Peoples R China
[5] Shanghai Ocean Univ, Coll Food Sci & Technol, 999 Hucheng Ring Rd, Shanghai 201306, Peoples R China
基金
中国国家自然科学基金;
关键词
VACCINES; CELL; IMMUNOTHERAPY;
D O I
10.1093/database/baaa004
中图分类号
Q [生物科学];
学科分类号
07 ; 0710 ; 09 ;
摘要
Neoantigens can function as actual antigens to facilitate tumor rejection, which play a crucial role in cancer immunology and immunotherapy. Emerging evidence revealed that neoantigens can be used to develop personalized, cancer-specific vaccines. To date, large numbers of immunogenomic peptides have been computationally predicted to be potential neoantigens. However, experimental validation remains the gold standard for potential clinical application. Experimentally validated neoantigens are rare and mostly appear scattered among scientific papers and various databases. Here, we constructed dbPepNeo, a specific database for human leukocyte antigen class I (HLA-I) binding neoantigen peptides based on mass spectrometry (MS) validation or immunoassay in human tumors. According to the verification methods of these neoantigens, the collection of peptides was classified as 295 high confidence, 247 medium confidence and 407 794 low confidence neoantigens, respectively. This can serve as a valuable resource to aid further screening for effective neoantigens, optimize a neoantigen prediction pipeline and study T-cell receptor (TCR) recognition. Three applications of dbPepNeo are shown. In summary, this work resulted in a platform to promote the screening and confirmation of potential neoantigens in cancer immunotherapy. Database URL: vvwvv.biostatistics.online/dbPepNeo/.
引用
收藏
页数:10
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