TCRex: detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires

Gielis, Sofie; Moris, Pieter; Bittremieux, Wout; Neuter, Nicolas De; Ogunjimi, Benson; Laukens, Kris; Meysman, Pieter

doi:10.1101/373472

Cited by 25 publications

(14 citation statements)

References 32 publications

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“…Although we do not fully understand the factors underlying TCR-pMHC recognition, recent studies have shown that T cells binding to a particular pMHC share common TCR sequence features and, in select cases, it is possible to predict the binding probability of an unseen TCR sequence based on learned TCR sequence features (13)(14)(15)(16)(17)(18)(19)(20)(21). However, these studies either were limited by the quantity and diversity of training data generated by traditional single multimer sorting or antigen reexposure assays or suffered from the challenging normalization issues associated with multi-omic characterization of T cells (22).…”

Section: Introductionmentioning

confidence: 99%

“…As next-generation screening technologies have increased the volume of available TCR-pMHC binding data, robust classifiers to computationally validate and subsequently predict TCR-pMHCspecific recognition are desired. While the results from initial TCR-pMHC binding classifiers are encouraging, most of them were only trained using CDR loop sequences or -chain sequences alone and thus unable to learn the overall complex sequence patterns from full-length TCR sequences, resulting in suboptimal prediction accuracy for highly diverse pMHC binding TCRs (13,14,19,20). Leveraging the ability of deep learning algorithms to learn complex patterns, a couple of neural network-based classifiers were recently proposed (17,18) to uncover binding patterns in large, highly complex pMHC binding TCR sequences.…”

Section: Introductionmentioning

confidence: 99%

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A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

et al. 2021

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T cell receptor (TCR) antigen–specific recognition is essential for the adaptive immune system. However, building a TCR-antigen interaction map has been challenging due to the staggering diversity of TCRs and antigens. Accordingly, highly multiplexed dextramer-TCR binding assays have been recently developed, but the utility of the ensuing large datasets is limited by the lack of robust computational methods for normalization and interpretation. Here, we present a computational framework comprising a novel method, ICON (Integrative COntext-specific Normalization), for identifying reliable TCR-pMHC (peptide–major histocompatibility complex) interactions and a neural network–based classifier TCRAI that outperforms other state-of-the-art methods for TCR-antigen specificity prediction. We further demonstrated that by combining ICON and TCRAI, we are able to discover novel subgroups of TCRs that bind to a given pMHC via different mechanisms. Our framework facilitates the identification and understanding of TCR-antigen–specific interactions for basic immunological research and clinical immune monitoring.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

et al. 2021

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“…Most are constructed based on data from the IEDB, VDJdb, and/or McPAS-TCR and, in addition to the epitope information, make use of either CDR3β sequences alone [13][14][15] , a mixture of CDR3α and CDR3β sequences 16 , or smaller data sets entailing all 6 CDR3 sequences and potentially additional cellular information 17,18 . Methodologically, the different studies range from simple CDR3β alignmentbased methods 19,22 , over CDR similarity-weighted distances such as TCRdist 7 , k-mer feature spaces in combination with PCA and decision trees (SETE 13 ), random forests 20,21 such as TCRex 23 , CNN-based (ImRex) 16 , and Gaussian process classification methods (TCRGP 17 ), to more complex approaches integrating natural language processing (NLP) methods (ERGO 14 ). The overall conclusion from these earlier works is that while the prediction of TCR specificity is feasible, the volume and accuracy of current data limit the performance of the developed models.…”

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confidence: 99%

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

et al. 2021

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Prediction of T-cell receptor (TCR) interactions with MHC-peptide complexes remains highly challenging. This challenge is primarily due to three dominant factors: data accuracy, data scarceness, and problem complexity. Here, we showcase that “shallow” convolutional neural network (CNN) architectures are adequate to deal with the problem complexity imposed by the length variations of TCRs. We demonstrate that current public bulk CDR3β-pMHC binding data overall is of low quality and that the development of accurate prediction models is contingent on paired α/β TCR sequence data corresponding to at least 150 distinct pairs for each investigated pMHC. In comparison, models trained on CDR3α or CDR3β data alone demonstrated a variable and pMHC specific relative performance drop. Together these findings support that T-cell specificity is predictable given the availability of accurate and sufficient paired TCR sequence data. NetTCR-2.0 is publicly available at https://services.healthtech.dtu.dk/service.php?NetTCR-2.0.

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“…Our co-authors' group recently implemented a phase I clinical trial to evaluate the effects of CD200-blockade in the GBM6-AD lysate vaccine in patients with recurrent high-grade gliomas (NCT04642937). To improve the design of our future vaccine studies, the use of novel algorithms developed for the identification of target epitopes recognized by the TCR repertoire (31,32) may allow us to identify the antigenic targets.…”

Section: Discussionmentioning

confidence: 99%

Randomized trial of neoadjuvant vaccination with tumor-cell lysate induces T cell response in low-grade gliomas

Ogino¹,

Taylor²,

Nejo³

et al. 2022

Journal of Clinical Investigation

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BACKGROUND.Long-term prognosis of WHO grade II low-grade glioma (LGG) is poor secondary to risk of recurrence and malignant transformation into high-grade glioma. Given the relatively intact immune system of patients with LGG and the slow tumor growth rate, vaccines are an attractive treatment strategy. METHODS.We conducted a pilot study to evaluate the safety and immunological effects of vaccination with GBM6-AD, lysate of an allogeneic glioblastoma stem cell line, with poly-ICLC in patients with LGG.Patients were randomized to receive the vaccines before surgery (Arm 1) or not (Arm 2) and all patients received adjuvant vaccine. Co-primary outcomes were to evaluate the safety and immune response in the tumor. RESULTS: A total of 17 eligible patients were enrolled -nine into Arm 1 and eight into Arm 2. This regimen was well-tolerated with no regimen-limiting toxicity. Neoadjuvant vaccination induced upregulation of type-1 cytokines and chemokines, and increased activated CD8 + T-cells in peripheral blood. Single-cell RNA/TCR-sequencing detected CD8 + T-cell clones that expanded with effector phenotype and migrated into tumor microenvironment (TME) in response to neoadjuvant vaccination. Mass cytometric analyses detected increased tissue resident-like CD8 + T-cells with effector memory phenotype in TME following the neoadjuvant vaccination. CONCLUSION. The current regimen induces effector CD8 + T-cell response in peripheral blood and 5 enables vaccine-reactive CD8 + T-cells to migrate into TME. Further refinements of the regimen may have to be integrated into future strategies. TRIAL REGISTRATION. ClinicalTrials.gov NCT02549833.

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TCRex: detection of enriched T cell epitope specificity in full T cell receptor sequence repertoires

Cited by 25 publications

References 32 publications

A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

A framework for highly multiplexed dextramer mapping and prediction of T cell receptor sequences to antigen specificity

NetTCR-2.0 enables accurate prediction of TCR-peptide binding by using paired TCRα and β sequence data

Randomized trial of neoadjuvant vaccination with tumor-cell lysate induces T cell response in low-grade gliomas

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