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

Wu, Jingcheng; Chen, Wenfan; Zhou, Yuxuan; Chi, Ying; Hua, Xiansheng; Wu, Jian; Gu, Xun; Chen, Shuqing; Zhou, Zhan

doi:10.1101/2022.07.28.501872

Cited by 3 publications

(2 citation statements)

References 31 publications

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“…Therefore, we applied epiTCR model to predict the binding between TCR and tumor neoantigens ( Supplementary Table S5 ). From our initial datasets, the neoantigens were retrieved by searching through five curated databases: TSNAdb ( Wu et al 2022 , 2018 ), NeoPeptide ( Zhou et al 2019 ), dbPepNeo ( Tan et al 2020 ; Lu et al 2022 ), NEPdb ( Xia et al 2021 ), and TANTIGEN ( Olsen et al 2017 ; Zhang et al 2021a ). All databases report antigens from published works and TSNAdb additionally includes the mutations found in The Cancer Genome Atlas (TCGA), IEDB ( Vita et al 2019 ), and The International Cancer Genome Consortium Data Portal (ICGC) ( Zhang et al 2019 ).…”

Section: Resultsmentioning

confidence: 99%

epiTCR: a highly sensitive predictor for TCR–peptide binding

et al. 2023

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Motivation Predicting the binding between T-cell receptor (TCR) and peptide presented by HLA molecule is a highly challenging task and a key bottleneck in the development of immunotherapy. Existing prediction tools, despite exhibiting good performance on the datasets they were built with, suffer from low true positive rates when used to predict epitopes capable of eliciting T-cell responses in patients. Therefore, an improved tool for TCR-peptide prediction built upon a large dataset combining existing publicly available data is still needed. Results We collected data from five public databases (IEDB, TBAdb, VDJdb, McPAS-TCR, and 10X) to form a dataset of > 3 million TCR-peptide pairs, 3.27% of which were binding interactions. We proposed epiTCR, a Random Forest-based method dedicated to predicting the TCR-peptide interactions. epiTCR used simple input of TCR CDR3β sequences and antigen sequences, which are encoded by flattened BLOSUM62. epiTCR performed with AUC (0.98) and higher sensitivity (0.94) than other existing tools (NetTCR, Imrex, ATM-TCR, and pMTnet), while maintaining comparable prediction specificity (0.9). We identified seven epitopes that contributed to 98.67% of false positives predicted by epiTCR and exerted similar effects on other tools. We also demonstrated a considerable influence of peptide sequences on prediction, highlighting the need for more diverse peptides in a more balanced dataset. In conclusion, epiTCR is among the most well-performing tools thanks to the use of combined data from public sources and its use will contribute to the quest in identifying neoantigens for precision cancer immunotherapy. Availability epiTCR is available on GitHub (https://github.com/ddiem-ri-4D/epiTCR). Supplementary information Supplementary data are available at Bioinformatics online.

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Section: Resultsmentioning

confidence: 99%

epiTCR: a highly sensitive predictor for TCR–peptide binding

et al. 2023

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“…The Immune Epitope Database (IEDB) ( 4 ) is a globally accessible gateway to experimentally validated immune epitopes, while other databases, such as AntiJen ( 5 ) and caped ( 6 ) focus on curated cancer epitopes from research manuscripts. In addition, the GNIFdb ( 7 ) and TSNAdb databases ( 8 , 9 ) utilize computational approaches to predict putative neoAg based on high-frequency mutations detected in cancers. However, there is currently no antigen database dedicated to neoAgs derived from microsatellite tracts.…”

Section: Introductionmentioning

confidence: 99%

MONET: a database for prediction of neoantigens derived from microsatellite loci

Deng,

Sinha,

Vilar

2024

Front. Immunol.

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BackgroundMicrosatellite instability (MSI) secondary to mismatch repair (MMR) deficiency is characterized by insertions and deletions (indels) in short DNA sequences across the genome. These indels can generate neoantigens, which are ideal targets for precision immune interception. However, current neoantigen databases lack information on neoantigens arising from coding microsatellites. To address this gap, we introduce The MicrOsatellite Neoantigen Discovery Tool (MONET).MethodMONET identifies potential mutated tumor-specific neoantigens (neoAgs) by predicting frameshift mutations in coding microsatellite sequences of the human genome. Then MONET annotates these neoAgs with key features such as binding affinity, stability, expression, frequency, and potential pathogenicity using established algorithms, tools, and public databases. A user-friendly web interface (https://monet.mdanderson.org/) facilitates access to these predictions.ResultsMONET predicts over 4 million and 15 million Class I and Class II potential frameshift neoAgs, respectively. Compared to existing databases, MONET demonstrates superior coverage (>85% vs. <25%) using a set of experimentally validated neoAgs.ConclusionMONET is a freely available, user-friendly web tool that leverages publicly available resources to identify neoAgs derived from microsatellite loci. This systems biology approach empowers researchers in the field of precision immune interception.

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The Value of Microbes in Cancer Neoantigen Immunotherapy

Jing

2023

Pharmaceutics

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Tumor neoantigens are widely used in cancer immunotherapy, and a growing body of research suggests that microbes play an important role in these neoantigen-based immunotherapeutic processes. The human body and its surrounding environment are filled with a large number of microbes that are in long-term interaction with the organism. The microbiota can modulate our immune system, help activate neoantigen-reactive T cells, and play a great role in the process of targeting tumor neoantigens for therapy. Recent studies have revealed the interconnection between microbes and neoantigens, which can cross-react with each other through molecular mimicry, providing theoretical guidance for more relevant studies. The current applications of microbes in immunotherapy against tumor neoantigens are mainly focused on cancer vaccine development and immunotherapy with immune checkpoint inhibitors. This article summarizes the related fields and suggests the importance of microbes in immunotherapy against neoantigens.

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

Cited by 3 publications

References 31 publications

epiTCR: a highly sensitive predictor for TCR–peptide binding

epiTCR: a highly sensitive predictor for TCR–peptide binding

MONET: a database for prediction of neoantigens derived from microsatellite loci

The Value of Microbes in Cancer Neoantigen Immunotherapy

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