T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

Frank, Meredith; Lu, K.H.; Erdogan, Can; Han, Yi; Hu, Jian; Wang, Tao; Heymach, John V.; Zhang, Jianjun; Reuben, Alexandre

doi:10.1158/1078-0432.ccr-22-2469

Cited by 28 publications

(14 citation statements)

References 156 publications

(164 reference statements)

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“…The primary result of NCT03710265 in patients with solid tumors shows that in all 45 patients, ORR was 17.8% DCR was 40.0% after a median of 6 weeks of exposure [20]. Another phase I trial studying its efficacy on cervical cancer shows that ORR was 15.6% and DCR was 40.0% in 32 patients [21].…”

Section: Developing Drugsmentioning

confidence: 99%

Recent update of PD-L1 inhibitor therapy and companion diagnostics

Jiang

2023

Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022)

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PD1/PD-L1 is a suppressive pathway of immune response which can be exploited by cancers and cause immune evasion. PD-L1 inhibitors are developed to block PD1/PD-L1 interaction and maintain durable anti-tumor immune responses to improve patients' conditions. The therapeutic effects and toxicity of PD-L1 inhibitors have been researched in multiple clinical studies showing favorable results in particular populations. Atezolizumab, avelumab, and durvalumab have been granted approval by FDA, and more novel drugs are undergoing clinical trials. Biomarkers including PD-L1, TMB, TIL, and companion diagnostics are being researched for the prediction and identification of eligible patients. This paper discusses the recent development and notable information of PD-L1 inhibitors including the FDA-approved therapies, developing therapies, biomarkers, and companion diagnostics.

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Section: Developing Drugsmentioning

confidence: 99%

Recent update of PD-L1 inhibitor therapy and companion diagnostics

Jiang

2023

Second International Conference on Biological Engineering and Medical Science (ICBioMed 2022)

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Section: Introductionmentioning

confidence: 99%

“…The collection of BCRs and TCRs in an individual, tissue, or cell subset is referred to as the adaptive immune receptor repertoire (AIRR). Characterizing AIRRs is relevant for the study of the immune state of individuals in health and following alterations such as vaccines (Kotagiri et al 2022;Safonova et al 2022), infectious diseases (Parameswaran et al 2013;Chen et al 2021), (auto)immune diseases (Bashford-Rogers et al 2013;Stern et al 2014;Vander Heiden et al 2017;Ramadoss and Robinson 2020), and cancer (Frank et al 2023).…”

Section: Introductionmentioning

confidence: 99%

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

Gabernet,

Marquez,

Bjornson

et al. 2024

Preprint

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Adaptive Immune Receptor Repertoire sequencing (AIRR-seq) is a valuable experimental tool to study the immune state in health and following immune challenges such as infectious diseases, (auto)immune diseases, and cancer. Several tools have been developed to reconstruct B cell and T cell receptor sequences from AIRR-seq data and infer B and T cell clonal relationships. However, currently available tools offer limited parallelization across samples, scalability or portability to high-performance computing infrastructures. To address this need, we developed nf-core/airrflow, an end-to-end bulk and single-cell AIRR-seq processing workflow which integrates the Immcantation Framework following BCR and TCR sequencing data analysis best practices. The Immcantation Framework is a comprehensive toolset, which allows the processing of bulk and single-cell AIRR-seq data from raw read processing to clonal inference. nf-core/airrflow is written in Nextflow and is part of the nf-core project, which collects community contributed and curated Nextflow workflows for a wide variety of analysis tasks. We assessed the performance of nf-core/airrflow on simulated sequencing data and show example results with real datasets. To demonstrate the applicability of nf-core/airrflow to the high-throughput processing of large AIRR-seq datasets, we validated and extended previously reported findings of convergent antibody responses to SARS-CoV-2 by analyzing 97 COVID-19 infected individuals and 99 healthy controls retrieved from public databases. nf-core/airrflow is available free of charge, under the MIT license on GitHub (https://github.com/nf-core/airrflow). Documentation and example results are available at https://nf-co.re/airrflow.

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“…The fast, cost-effective, and accurate identification of the binding between TCRs and T cell antigens will vastly enhance our understanding of T cells' roles in normal developmental processes and multiple diseases. Such information could also widely impact the design and implementation of various immunotherapies and disease diagnostics [1][2][3][4][5] , such as improvement of engineered T cell receptors for TCR T therapies and TCR-like drugs, antigen selection for tumor vaccines, and biomarkers for monitoring T cell function.…”

Section: Introductionmentioning

confidence: 99%

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

Han,

Yang,

Tian

et al. 2023

Preprint

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SUMMARYProfiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αβ T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

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T-Cell Receptor Repertoire Sequencing in the Era of Cancer Immunotherapy

Cited by 28 publications

References 156 publications

Recent update of PD-L1 inhibitor therapy and companion diagnostics

Recent update of PD-L1 inhibitor therapy and companion diagnostics

nf-core/airrflow: an adaptive immune receptor repertoire analysis workflow employing the Immcantation framework

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding

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