TCR sequencing: applications in immuno-oncology research

Sanromán, Ángel Fernández; Joshi, Kroopa; Au, Lewis; Chain, Benjamin M.; Turajlic, Samra

doi:10.1016/j.iotech.2023.100373

Cited by 8 publications

(14 citation statements)

References 68 publications

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“…The major limitations of this study include its relatively small sample sizes and the inherent limitations of TCR studies, including potential sample heterogeneity, cross‐platform variability, and the lack of direct functional insights 22 . However, TCR sequencing allows for uncovering T‐cell diversity, aiding in disease associations, and potentially informing immunotherapy strategies.…”

Section: Discussionmentioning

confidence: 99%

T‐cell receptor diversity and allergen sensitivity in childhood asthma and atopic dermatitis

Chen,

Hung,

Chung

et al. 2024

Pediatric Allergy Immunology

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BackgroundChildhood allergies of asthma and atopic dermatitis (AD) involve an overactive T‐cell immune response triggered by allergens. However, the impact of T‐cell receptor (TCR) repertoires on allergen sensitization and their role in mediating different phenotypes of asthma and AD in early childhood remains unclear.MethodsA total of 78 children, comprising 26 with asthma alone, 26 with AD alone, and 26 healthy controls (HC), were enrolled. TCR repertoire profiles were determined using a unique molecular identifier system for next‐generation sequencing. Integrative analyses of their associations with allergen‐specific IgE levels and allergies were performed.ResultsThe diversity in TCR alpha variable region (TRAV) genes of TCR repertoires and complementarity determining region 3 (CDR3) clonality in TRAV/TRBV (beta) genes were significantly higher in children with AD compared with those with asthma and HC (p < .05). Compared with HC, the expression of TRAV13‐1 and TRAV4 genes was significantly higher in both asthma and AD (p < .05), with a significant positive correlation with mite‐specific IgE levels (p < .01). In contrast, TRBV7‐9 gene expression was significantly lower in both asthma and AD (p < .01), with this gene showing a significant negative correlation with mite‐specific IgE levels (p < .01). Furthermore, significantly higher TRAV8‐3 gene expression, positively correlated with food‐specific IgE levels, was found in children with AD compared with those with asthma (p < .05).ConclusionIntegrated TCR repertoires analysis provides clinical insights into the diverse TCR genes linked to antigen specificity, offering potential for precision immunotherapy in childhood allergies.

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Section: Discussionmentioning

confidence: 99%

T‐cell receptor diversity and allergen sensitivity in childhood asthma and atopic dermatitis

Chen,

Hung,

Chung

et al. 2024

Pediatric Allergy Immunology

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“…Our data identify some baseline intra-tumoral characteristics that may drive response to treatment, such as TIL high diversity and increased TIL clustering in the pre-treatment biopsy. The debate regarding the primary mechanism underlying ICI response focuses on two hypotheses: “T-cell clonal replacement,” which involves the recruitment of novel T-cells into the tumor, and “T-cell clonal revival,” which suggests the reinvigoration of pre-existing TILs ( 40 ). Clonal replacement mechanism has been supported in studies that included patients with skin cancer and NSCLC which showed that anti-PD-1 therapy promotes the infiltration of T-cells from the blood into the tumor by inducing recognition of neoantigens different from those recognized by expanded TCR clones at baseline, and that patients with major pathological response showed substantial overlap between intra-tumoral and peripheral blood TCR clonotypes, with most expanded clones shared between the two compartments ( 41 , 42 ).…”

Section: Discussionmentioning

confidence: 99%

T-cell receptor determinants of response to chemoradiation in locally-advanced HPV16-driven malignancies

Nenclares,

Larkeryd,

Manodoro

et al. 2024

Front. Oncol.

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BackgroundThe effect of chemoradiation on the anti-cancer immune response is being increasingly acknowledged; however, its clinical implications in treatment responses are yet to be fully understood. Human papillomavirus (HPV)-driven malignancies express viral oncogenic proteins which may serve as tumor-specific antigens and represent ideal candidates for monitoring the peripheral T-cell receptor (TCR) changes secondary to chemoradiotherapy (CRT).MethodsWe performed intra-tumoral and pre- and post-treatment peripheral TCR sequencing in a cohort of patients with locally-advanced HPV16-positive cancers treated with CRT. An in silico computational pipeline was used to cluster TCR repertoire based on epitope-specificity and to predict affinity between these clusters and HPV16-derived epitopes.ResultsIntra-tumoral repertoire diversity, intra-tumoral and post-treatment peripheral CDR3β similarity clustering were predictive of response. In responders, CRT triggered an increase peripheral TCR clonality and clonal relatedness. Post-treatment expansion of baseline peripheral dominant TCRs was associated with response. Responders showed more baseline clustered structures of TCRs maintained post-treatment and displayed significantly more maintained clustered structures. When applying clustering by TCR-specificity methods, responders displayed a higher proportion of intra-tumoral TCRs predicted to recognise HPV16 peptides.ConclusionsBaseline TCR characteristics and changes in the peripheral T-cell clones triggered by CRT are associated with treatment outcome. Maintenance and boosting of pre-existing clonotypes are key elements of an effective anti-cancer immune response driven by CRT, supporting a paradigm in which the immune system plays a central role in the success of CRT in current standard-of-care protocols.

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“…This work also underlined the need for technologies for high-throughput paired sequencing of TCRs with known pMHC targets. The current optimal way to pair TCR α- and β- chain is through single-cell TCR sequencing (scTCR-Seq) ( 155 ). The authors of NetTCR-2.1 ( 156 ) provide lessons and guidance on how to develop models for TCR specificity predictions, how to best define negative data, and why it is recommended to apply similarity-based modeling, and include a performance evaluation as a function of “distance” to the training data when validating predictive power of ML-based approaches.…”

Section: T Cell Receptor Recognitionmentioning

confidence: 99%

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

Bulashevska,

Nacsa,

Lang

et al. 2024

Front. Immunol.

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Cancer immunotherapy has witnessed rapid advancement in recent years, with a particular focus on neoantigens as promising targets for personalized treatments. The convergence of immunogenomics, bioinformatics, and artificial intelligence (AI) has propelled the development of innovative neoantigen discovery tools and pipelines. These tools have revolutionized our ability to identify tumor-specific antigens, providing the foundation for precision cancer immunotherapy. AI-driven algorithms can process extensive amounts of data, identify patterns, and make predictions that were once challenging to achieve. However, the integration of AI comes with its own set of challenges, leaving space for further research. With particular focus on the computational approaches, in this article we have explored the current landscape of neoantigen prediction, the fundamental concepts behind, the challenges and their potential solutions providing a comprehensive overview of this rapidly evolving field.

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TCR sequencing: applications in immuno-oncology research

Cited by 8 publications

References 68 publications

T‐cell receptor diversity and allergen sensitivity in childhood asthma and atopic dermatitis

T‐cell receptor diversity and allergen sensitivity in childhood asthma and atopic dermatitis

T-cell receptor determinants of response to chemoradiation in locally-advanced HPV16-driven malignancies

Artificial intelligence and neoantigens: paving the path for precision cancer immunotherapy

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