dbPepNeo2.0: A Database for Human Tumor Neoantigen Peptides From Mass Spectrometry and TCR Recognition

Lu, Manman; Xu, Linfeng; Jian, Xingxing; Tan, Xiaofang; Zhao, Jingjing; Liu, Zhenhao; Liu, Chunyu; Chen, Lanming; Lin, Yong; Xie, Lu

doi:10.3389/fimmu.2022.855976

Cited by 17 publications

(14 citation statements)

References 53 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 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: Methodsmentioning

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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“…Previously, we have constructed a prediction tool of immunogenic neoantigens, namely DeepCNN-Ineo, aiming at screening those tumor neoantigens bound by HLA molecular and recognized by TCR sequences [19] . In this study, we obtained 108 RNA-seq files of peripheral blood, including 69 samples from COVID-19 patients and 39 samples from HD, to extract TCRβ immune repertoire, which was found to relate with the disease progression of COVID-19 samples.…”

Section: Introductionmentioning

confidence: 99%