Cancer Neoantigens

Schumacher, Ton N.; Scheper, Wouter; Kvistborg, Pia

doi:10.1146/annurev-immunol-042617-053402

Cited by 398 publications

(334 citation statements)

References 135 publications

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“…3b). Interestingly, the post treatment repertoire signature was the least predictive in the cohorts of mice that received checkpoint block inhibition (α-CTLA4), compatible with the notion that α -CTLA4 was acting by shaping the structural diversity of the repertoire towards immune recognition of personalized neoantigens, as has been suggested in prior work as a possible mechanism of action 44,45 . Finally, not only are supervised approaches valuable for their predictive utility but they can also provide a different and more informative featurization and description of the TCR repertoire guided by ground truth labels ( Fig.…”

supporting

confidence: 75%

DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires

Sidhom

Larman

Ross‐Macdonald

et al. 2018

Preprint

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Deep learning algorithms have been utilized to achieve enhanced performance in pattern-recognition tasks, such as in image and vocal recognition 1,2 . The ability to learn complex patterns in data has tremendous implications in the genomics and immunology worlds, where sequence motifs become learned 'features' that can be used to predict functionality, guiding our understanding of disease and basic biology 3-6 . T-cell receptor (TCR) sequencing assesses the diversity of the adaptive immune system, where complex structural patterns in the TCR can be used to model its antigenic interaction. We present DeepTCR, a broad collection of unsupervised and supervised deep learning methods able to uncover structure in highly complex and large TCR sequencing data by learning a joint representation of a given TCR by its CDR3 sequences, V/D/J gene usage, and HLA background in which the T-cells reside. We demonstrate the utility of deep learning to provide an improved 'featurization' of the TCR across multiple human and murine datasets, including improved classification of antigen-specific TCR's in both unsupervised and supervised learning tasks, understanding immunotherapy-related shaping of repertoire in the murine setting, and predicting response to checkpoint blockade immunotherapy from pre-treatment tumor biopsies in a clinical trial of melanoma. Our results show the flexibility and capacity for deep neural networks to handle the complexity of high-dimensional TCR genomic data for both descriptive and predictive purposes across basic science and clinical research.

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supporting

confidence: 75%

DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires

Sidhom

Larman

Ross‐Macdonald

et al. 2018

Preprint

View full text Add to dashboard Cite

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“…C ancer is a genetic disease, with the growth of tumor cells initiated by mutations that activate oncogenic drivers and disable tumor suppressors. Recent studies have demonstrated that tumor neoantigens can be derived de novo from the expression of genetic mutations and presented in major histocompatibility complexes (MHC) on tumor cells, and endogenous T cell responses against neoantigens can be naturally activated in cancer patients [1][2][3][4] . However, only a small number of nonsynonymous mutations expressed in tumors can be adequately presented as neoantigens for which the T cell response can be mounted 5,6 .…”

mentioning

confidence: 99%

An engineered oncolytic virus expressing PD-L1 inhibitors activates tumor neoantigen-specific T cell responses

et al. 2020

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Oncolytic viruses offer an in situ vaccination approach to activate tumor-specific T cell responses. However, the upregulation of PD-L1 expression on tumor cells and immune cells leads to tumor resistance to oncolytic immunotherapy. In this study, we generate an engineered oncolytic virus that coexpresses a PD-L1 inhibitor and GM-CSF. We find that the oncolytic virus is able to secrete the PD-L1 inhibitor that systemically binds and inhibits PD-L1 on tumor cells and immune cells. Importantly, the intratumoral injection with the oncolytic virus overcomes PD-L1-mediated immunosuppression during both the priming and effector phases, provokes systemic T cell responses against dominant and subdominant neoantigen epitopes derived from mutations, and leads to an effective rejection of both virus-injected and distant tumors. In summary, this engineered oncolytic virus is able to activate tumor neoantigen-specific T cell responses, providing a potent, individual tumor-specific oncolytic immunotherapy for cancer patients, especially those resistant to PD-1/PD-L1 blockade therapy.

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“…Screening of immunogenic peptides which are presented by specific MHC I molecules is crucial for the development of vaccines for infection diseases or cancer immunotherapy and for T-cell immune response evaluation (45)(46)(47)(48)(49)(50)(51). Recently, several pre-clinical and clinical studies have shown that vaccines targeting predicted personal tumor neoantigens are feasibility, safety, and immunogenicity (45,50,(52)(53)(54)(55). Adoptive T cell therapy that specifically recognize neoantigens has mediated substantial objective clinical regressions in patients with melanoma and metastatic breast cancer (56)(57)(58).…”

Section: Discussionmentioning

confidence: 99%

Divergent Peptide Presentations of HLA-A*30 Alleles Revealed by Structures With Pathogen Peptides

et al. 2019

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Human leukocyte antigen (HLA) alleles have a high degree of polymorphism, which determines their peptide-binding motifs and subsequent T-cell receptor recognition. The simplest way to understand the cross-presentation of peptides by different alleles is to classify these alleles into supertypes. A1 and A3 HLA supertypes are widely distributed in humans. However, direct structural and functional evidence for peptide presentation features of key alleles (e.g., HLA-A * 30:01 and -A * 30:03) are lacking. Herein, the molecular basis of peptide presentation of HLA-A * 30:01 and -A * 30:03 was demonstrated by crystal structure determination and thermostability measurements of complexes with T-cell epitopes from influenza virus (NP44), human immunodeficiency virus (RT313), and Mycobacterium tuberculosis (MTB). When binding to the HIV peptide, RT313, the PΩ-Lys anchoring modes of HLA-A * 30:01, and -A * 30:03 were similar to those of HLA-A * 11:01 in the A3 supertype. However, HLA-A * 30:03, but not -A * 30:01, also showed binding with the HLA * 01:01-favored peptide, NP44, but with a specific structural conformation. Thus, different from our previous understanding, HLA-A * 30:01 and -A * 30:03 have specific peptide-binding characteristics that may lead to their distinct supertype-featured binding peptide motifs. Moreover, we also found that residue 77 in the F pocket was one of the key residues for the divergent peptide presentation characteristics of HLA-A * 30:01 and -A * 30:03. Interchanging residue 77 between HLA-A * 30:01 and HLA-A * 30:03 switched their presented peptide profiles. Our results provide important recommendations for screening virus and tumor-specific peptides among the population with prevalent HLA supertypes for vaccine development and immune interventions.

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Cancer Neoantigens

Cited by 398 publications

References 135 publications

DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires

DeepTCR: a deep learning framework for understanding T-cell receptor sequence signatures within complex T-cell repertoires

An engineered oncolytic virus expressing PD-L1 inhibitors activates tumor neoantigen-specific T cell responses

Divergent Peptide Presentations of HLA-A*30 Alleles Revealed by Structures With Pathogen Peptides

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