NEPdb: A Database of T-Cell Experimentally-Validated Neoantigens and Pan-Cancer Predicted Neoepitopes for Cancer Immunotherapy

Xia, Jiaqi; Bai, Peng; Fan, Weiliang; Li, Qi Ming; Li, Yongzheng; Wang, Dehe; Yin, Lei; Zhou, Yu

doi:10.3389/fimmu.2021.644637

Cited by 52 publications

(41 citation statements)

References 25 publications

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“…We used a fully manually annotated neoantigen database, NEPdb ( 57 ), newly published in 2021 to demonstrate that our FIONA-I model substantially improves the positive predictive value (PPV) of neoantigen prediction compared to the antigen presentation model. All MHC-II neoantigen data entries containing DP, DQ, and DR alleles were retrieved from the NEPdb, which contains 182 positive and 3,508 negative epitopes across 31 different MHC-II subtypes ( Supplementary Table 4 ).…”

Section: Resultsmentioning

confidence: 99%

A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity

Xu¹,

Wang²,

Fei³

2022

Front. Oncol.

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In the past decade, the substantial achievements of therapeutic cancer vaccines have shed a new light on cancer immunotherapy. The major challenge for designing potent therapeutic cancer vaccines is to identify neoantigens capable of inducing sufficient immune responses, especially involving major histocompatibility complex (MHC)-II epitopes. However, most previous studies on T-cell epitopes were focused on either ligand binding or antigen presentation by MHC rather than the immunogenicity of T-cell epitopes. In order to better facilitate a therapeutic vaccine design, in this study, we propose a revolutionary new tool: a convolutional neural network model named FIONA (Flexible Immunogenicity Optimization Neural-network Architecture) trained on IEDB datasets. FIONA could accurately predict the epitopes presented by the given specific MHC-II subtypes, as well as their immunogenicity. By leveraging the human leukocyte antigen allele hierarchical encoding model together with peptide dense embedding fusion encoding, FIONA (with AUC = 0.94) outperforms several other tools in predicting epitopes presented by MHC-II subtypes in head-to-head comparison; moreover, FIONA has unprecedentedly incorporated the capacity to predict the immunogenicity of epitopes with MHC-II subtype specificity. Therefore, we developed a reliable pipeline to effectively predict CD4+ T-cell immune responses against cancer and infectious diseases.

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Section: Resultsmentioning

confidence: 99%

A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity

Xu¹,

Wang²,

Fei³

2022

Front. Oncol.

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“…The validated neoantigens were collected not only from several neoantigen databases (dbPepNeo [19,20], NeoPeptide [21], NEPdb [22], CAPD [23]) but also from published literature through data mining. For the neoantigens without gene or mutation information, BLAST was used to figure out the mutated genes and the positions of somatic mutations at proteins.…”

Section: Experimentally Validated Neoantigensmentioning

confidence: 99%

TSNAdb v2.0: the updated version of tumor-specific neoantigen database

Chen

Zhou

et al. 2022

Preprint

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Tumor neoantigens have been well-acknowledged as ideal targets for tumor immunotherapy in recent years. With the deepening of research on neoantigen-based tumor immunotherapy, comprehensive neoantigen databases are urgently needed to meet the growing demand for clinical studies. We have built the Tumor-Specific NeoAntigen Database (TSNAdb v1.0) previously, which has attracted wide attention. In this study, we provide an updated version of the Tumor-Specific NeoAntigen Database (TSNAdb v2.0) with several new features including 1) taking stricter criteria for neoantigen identification. 2) providing predicted neoantigens derived from three types of somatic mutations. 3) collecting experimentally validated neoantigens and classifying them according to the evidence. TSNAdb v2.0 is freely available at https://pgx.zju.edu.cn/tsnadb/.

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“…However, none of these databases were designed to support glioma studies by integrating neoantigen intrinsic features in different glioma subtypes. Specifically, dbPepNeo ( 21 ) and NEPdb ( 24 ) focused on neoantigens manually curated from experimentally supported human tumor neoantigens. Immune Epitope Database (IEDB) ( 22 ), a gateway to global immune epitope information, was designed for general research purposes, mainly providing experimentally validated information of immune epitopes.…”

Section: Introductionmentioning

confidence: 99%

GNIFdb: a neoantigen intrinsic feature database for glioma

Sun

et al. 2022

Database

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Neoantigens are mutation-containing immunogenic peptides from tumor cells. Neoantigen intrinsic features are neoantigens’ sequence-associated features characterized by different amino acid descriptors and physical–chemical properties, which have a crucial function in prioritization of neoantigens with immunogenic potentials and predicting patients with better survival. Different intrinsic features might have functions to varying degrees in evaluating neoantigens’ potentials of immunogenicity. Identification and comparison of intrinsic features among neoantigens are particularly important for developing neoantigen-based personalized immunotherapy. However, there is still no public repository to host the intrinsic features of neoantigens. Therefore, we developed GNIFdb, a glioma neoantigen intrinsic feature database specifically designed for hosting, exploring and visualizing neoantigen and intrinsic features. The database provides a comprehensive repository of computationally predicted Human leukocyte antigen class I (HLA-I) restricted neoantigens and their intrinsic features; a systematic annotation of neoantigens including sequence, neoantigen-associated mutation, gene expression, glioma prognosis, HLA-I subtype and binding affinity between neoantigens and HLA-I; and a genome browser to visualize them in an interactive manner. It represents a valuable resource for the neoantigen research community and is publicly available at http://www.oncoimmunobank.cn/index.php. Database URL http://www.oncoimmunobank.cn/index.php

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NEPdb: A Database of T-Cell Experimentally-Validated Neoantigens and Pan-Cancer Predicted Neoepitopes for Cancer Immunotherapy

Cited by 52 publications

References 25 publications

A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity

A Highly Effective System for Predicting MHC-II Epitopes With Immunogenicity

TSNAdb v2.0: the updated version of tumor-specific neoantigen database

GNIFdb: a neoantigen intrinsic feature database for glioma

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