Computational Prediction and Validation of Tumor-Associated Neoantigens

Roudko, Vladimir; Greenbaum, Benjamin; Bhardwaj, Nina

doi:10.3389/fimmu.2020.00027

Cited by 95 publications

(84 citation statements)

References 152 publications

(93 reference statements)

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“…Evaluating performance against cancer neoantigen datasets A key application for immunogenicity classifiers is to predict immunogenic cancer neoantigens that are capable of activating CTLs for potential use as vaccine targets for personalised cancer immunotherapies 3,23 . The datasets that are used to train these models largely consist of pathogenic peptides with substantial sequence differences from the human proteome.…”

Section: Evaluating Performance Of Immunogenicity Models In Predictinmentioning

confidence: 99%

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

Buckley

Lee

et al. 2020

Preprint

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T cell recognition of a cognate peptide-MHC complex (pMHC) presented on the surface of infected or malignant cells are of utmost importance for mediating robust and long-term immune responses. Accurate predictions of cognate pMHC targets for T Cell Receptors (TCR) would greatly facilitate identification of vaccine targets for both pathogenic diseases as well as personalized cancer immunotherapies. Predicting immunogenic epitopes therefore has been at the centre of intensive research for the past decades but has proven challenging. Although numerous models have been proposed, performance of these models has not been systematically evaluated and their success rate in predicting epitopes for humans has not been measured and compared.In this study, we curate and present a ‘standard-dataset’ of class I MHC epitopes for evaluating the performance of immunogenicity classifiers. Using this data, we present a systematic evaluation of performance of several publicly available models which are commonly used to predict CD8+ T cell epitopes in the context of both pathogens and cancers. The benchmarking is performed in two different settings: a pan-HLA setting in which all models are trained and evaluated by a pool of peptides from multiple HLA types, and a HLA-specific setting in which models are trained and evaluated by peptides from HLA-A02:01. In the pan-HLA setting, we observe that the best performing model achieves 75.6% accuracy suggesting considerable room for improvement. In the HLA-specific setting we observe a substantial reduction in performance with a mean of −11.9%. Our work shows that existing models exhibit suboptimal performance for predicting immunogenic cancer neoantigens. We then further interrogate the underlying problems of model predictions and highlight HLA-bias as a main source of variation amongst other issues associated with the design of models and/or to their training data.

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Section: Evaluating Performance Of Immunogenicity Models In Predictinmentioning

confidence: 99%

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

Buckley

Lee

et al. 2020

Preprint

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“…Implementation of NGS and bioinformatics in the neo-antigen identification workflow has led to discovery of the source of neo-antigens, including cancer specific overexpression, alternative exon splicing, intron retention, gene fusions in addition to SNVs and INDELs [ 168 ]. MS and T cell reactivity studies have confirmed these findings.…”

Section: Future Perspectivesmentioning

confidence: 99%

“…Nonetheless, this selection bias withholds the risk of neglecting other potentially interesting neo-antigens. Furthermore, multiple training sets are available for frequent HLA types (e.g., HLA-A2), while training sets for infrequent HLA types are largely missing [ 74 , 168 ]. Implementing biological aspects of protein-to-peptide processing and HLA-peptide binding into the training algorithms is another strategy to improve in silico prediction of valid neo-epitopes.…”

Section: Future Perspectivesmentioning

confidence: 99%

Neo-Antigen mRNA Vaccines

et al. 2020

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The interest in therapeutic cancer vaccines has caught enormous attention in recent years due to several breakthroughs in cancer research, among which the finding that successful checkpoint blockade treatments reinvigorate neo-antigen-specific T cells and that successful adoptive cell therapies are directed towards neo-antigens. Neo-antigens are cancer-specific antigens, which develop from somatic mutations in the cancer cell genome that can be highly immunogenic and are not subjected to central tolerance. As the majority of neo-antigens are unique to each patient’s cancer, a vaccine technology that is flexible and potent is required to develop personalized neo-antigen vaccines. In vitro transcribed mRNA is such a technology platform and has been evaluated for delivery of neo-antigens to professional antigen-presenting cells both ex vivo and in vivo. In addition, strategies that support the activity of T cells in the tumor microenvironment have been developed. These represent a unique opportunity to ensure durable T cell activity upon vaccination. Here, we comprehensively review recent progress in mRNA-based neo-antigen vaccines, summarizing critical milestones that made it possible to bring the promise of therapeutic cancer vaccines within reach.

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“…More recently, the power of whole exome sequencing and RNA sequencing, aligned with improvements in algorithms that predict MHC binding sites and proteolysis by enzymes involved in antigen processing, has led to a surge in the identification of antigens that are specific to an individual patient's tumor. 45,46 There is a healthy debate as to the best class of antigen to include in vaccine development. Shared antigens have the advantage These variations influence the quality and quantity of any T cell response and the extent to which tumors might be edited to lose antigen expression in response to immune recognition.…”

Section: A Role For Cancer Vaccines In the Future Immunotherapy Repermentioning

confidence: 99%

Fundamentals of Cancer Immunology and Their Application to Cancer Vaccines

Bullock

2020

Clinical Translational Sci

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The capacity of the immune system to influence tumor progression has been a long‐standing notion that first generated clinical traction over a 100 years ago when Dr. William Coley injected disaggregated bacterial components into sarcomas and noted that the ensuing inflammation commonly associated with tumor regression. 1 Since then, our understanding of the individual components and the overall interaction of the immune system has expanded exponentially. This has led to the development of a robust understanding of how components of innate and adaptive immunity recognize and respond to tumors and leveraging this information for the development of tumor immunotherapies. However, clinical failures have also deepened our knowledge of how tumors might adapt/be selected to avoid or inhibit immune responses, which, in turn, has led to the further iteration of immunotherapies. In this tutorial, the established elements of tumor immunity are explained, and areas where our knowledge base is too thin is highlighted. The principles of tumor immunity that guide the development of cancer vaccines are further illustrated, and potential considerations of how to integrate cancer vaccines with conventional therapies and other immunotherapies are proposed.

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Computational Prediction and Validation of Tumor-Associated Neoantigens

Cited by 95 publications

References 152 publications

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

Evaluating performance of existing computational models in predicting CD8+ T cell pathogenic epitopes and cancer neoantigens

Neo-Antigen mRNA Vaccines

Fundamentals of Cancer Immunology and Their Application to Cancer Vaccines

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