Focusing in on T Cell Cross-Reactivity

Mandl, Judith N.; Germain, Ronald N.

doi:10.1016/j.cell.2014.05.002

Cited by 7 publications

(5 citation statements)

References 6 publications

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“…Thus, the TCR-repertoire has been termed unlimited. However, the relation between the possibility of recombination of the TCR and the number of universally possible peptides implies an imperative TCR promiscuity: 10 11 human TCRs have to match with 10 20 peptides [73] (Fig. 3).…”

Section: Reviewmentioning

confidence: 99%

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Precision medicine in pediatric oncology

et al. 2018

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Outcome in treatment of childhood cancers has improved dramatically since the 1970s. This success was largely achieved by the implementation of cooperative clinical research trial groups that standardized and developed treatment of childhood cancer. Nevertheless, outcome in certain types of malignancies is still unfavorable. Intensification of conventional chemotherapy and radiotherapy improved outcome only marginally at the cost of acute and long-term side effects. Hence, it is necessary to develop targeted therapy strategies.Here, we review the developments and perspectives in precision medicine in pediatric oncology with a special focus on targeted drug therapies like kinase inhibitors and inducers of apoptosis, the impact of cancer genome sequencing and immunotherapy.

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Section: Reviewmentioning

confidence: 99%

“…Hence, a necessary feature of a TCR repertoire is that a T-cell is able to recognize and respond to many peptides, but one TCR only recognizes and responds to peptides closely related to the original agonist peptide (similar colors representing peptide relatedness). Modified from Mandl and Germain 2014 [73]…”

Section: Reviewmentioning

confidence: 99%

Precision medicine in pediatric oncology

et al. 2018

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“…Considering that most documented cases of T-cell off-target toxicity have been associated with molecular mimicry ( 20 , 21 , 52 , 70 ), we expected that validated target/off-target pairs should in general present low relatedness scores. In this context, the relatedness distribution obtained in the Monte Carlo simulation described in section 4.3 can both be used to understand the dispersion of validated cases, and to identify confidence boundaries in our predictions (i.e., statistical threshold).…”

Section: Resultsmentioning

confidence: 99%

“…For instance, engineering a T-cell receptor is a challenging task that involves potentially conflicting goals, such as enhancing the T-cell response to the tumor-derived peptide, while avoiding side effects caused by T-cell cross-reactivity (16)(17)(18). T-cell cross-reactivity is the ability of a single T-cell clonotype to recognize and respond to multiple heterologous peptide-HLA (pHLA) complexes (19)(20)(21). From an evolutionary perspective, T-cell cross-reactivity is necessary to maximize the range of unrelated antigens/pathogens that can be recognized by a limited pool of T-cells (i.e., to mediate heterologous immunity between pathogens) (22-24).…”

Section: Introductionmentioning

confidence: 99%

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

Fonseca

Antunes

2023

Front. Immunol.

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T-cell-based immunotherapies hold tremendous potential in the fight against cancer, thanks to their capacity to specifically targeting diseased cells. Nevertheless, this potential has been tempered with safety concerns regarding the possible recognition of unknown off-targets displayed by healthy cells. In a notorious example, engineered T-cells specific to MAGEA3 (EVDPIGHLY) also recognized a TITIN-derived peptide (ESDPIVAQY) expressed by cardiac cells, inducing lethal damage in melanoma patients. Such off-target toxicity has been related to T-cell cross-reactivity induced by molecular mimicry. In this context, there is growing interest in developing the means to avoid off-target toxicity, and to provide safer immunotherapy products. To this end, we present CrossDome, a multi-omics suite to predict the off-target toxicity risk of T-cell-based immunotherapies. Our suite provides two alternative protocols, i) a peptide-centered prediction, or ii) a TCR-centered prediction. As proof-of-principle, we evaluate our approach using 16 well-known cross-reactivity cases involving cancer-associated antigens. With CrossDome, the TITIN-derived peptide was predicted at the 99+ percentile rank among 36,000 scored candidates (p-value < 0.001). In addition, off-targets for all the 16 known cases were predicted within the top ranges of relatedness score on a Monte Carlo simulation with over 5 million putative peptide pairs, allowing us to determine a cut-off p-value for off-target toxicity risk. We also implemented a penalty system based on TCR hotspots, named contact map (CM). This TCR-centered approach improved upon the peptide-centered prediction on the MAGEA3-TITIN screening (e.g., from 27th to 6th, out of 36,000 ranked peptides). Next, we used an extended dataset of experimentally-determined cross-reactive peptides to evaluate alternative CrossDome protocols. The level of enrichment of validated cases among top 50 best-scored peptides was 63% for the peptide-centered protocol, and up to 82% for the TCR-centered protocol. Finally, we performed functional characterization of top ranking candidates, by integrating expression data, HLA binding, and immunogenicity predictions. CrossDome was designed as an R package for easy integration with antigen discovery pipelines, and an interactive web interface for users without coding experience. CrossDome is under active development, and it is available at https://github.com/AntunesLab/crossdome.

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“…However, T cell cross-reactivity is not determined only by sequence similarity. Recent studies have demonstrated that T cell cross-reactivity is also determined by structural similarities of unrelated peptide-HLA (pHLA) complexes (39)(40)(41).…”

Section: Introductionmentioning

confidence: 99%

Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2

et al. 2022

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Although not being the first viral pandemic to affect humankind, we are now for the first time faced with a pandemic caused by a coronavirus. The Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) has been responsible for the COVID-19 pandemic, which caused more than 4.5 million deaths worldwide. Despite unprecedented efforts, with vaccines being developed in a record time, SARS-CoV-2 continues to spread worldwide with new variants arising in different countries. Such persistent spread is in part enabled by public resistance to vaccination in some countries, and limited access to vaccines in other countries. The limited vaccination coverage, the continued risk for resistant variants, and the existence of natural reservoirs for coronaviruses, highlight the importance of developing additional therapeutic strategies against SARS-CoV-2 and other coronaviruses. At the beginning of the pandemic it was suggested that countries with Bacillus Calmette-Guérin (BCG) vaccination programs could be associated with a reduced number and/or severity of COVID-19 cases. Preliminary studies have provided evidence for this relationship and further investigation is being conducted in ongoing clinical trials. The protection against SARS-CoV-2 induced by BCG vaccination may be mediated by cross-reactive T cell lymphocytes, which recognize peptides displayed by class I Human Leukocyte Antigens (HLA-I) on the surface of infected cells. In order to identify potential targets of T cell cross-reactivity, we implemented an in silico strategy combining sequence-based and structure-based methods to screen over 13,5 million possible cross-reactive peptide pairs from BCG and SARS-CoV-2. Our study produced (i) a list of immunogenic BCG-derived peptides that may prime T cell cross-reactivity against SARS-CoV-2, (ii) a large dataset of modeled peptide-HLA structures for the screened targets, and (iii) new computational methods for structure-based screenings that can be used by others in future studies. Our study expands the list of BCG peptides potentially involved in T cell cross-reactivity with SARS-CoV-2-derived peptides, and identifies multiple high-density “neighborhoods” of cross-reactive peptides which could be driving heterologous immunity induced by BCG vaccination, therefore providing insights for future vaccine development efforts.

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Focusing in on T Cell Cross-Reactivity

Cited by 7 publications

References 6 publications

Precision medicine in pediatric oncology

Precision medicine in pediatric oncology

CrossDome: an interactive R package to predict cross-reactivity risk using immunopeptidomics databases

Large-Scale Structure-Based Screening of Potential T Cell Cross-Reactivities Involving Peptide-Targets From BCG Vaccine and SARS-CoV-2

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