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      CBFB-MYH11 fusion neoantigen enables T cell recognition and killing of acute myeloid leukemia

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          Abstract

          Proteins created from recurrent fusion genes like CBFB-MYH11 are prevalent in acute myeloid leukemia (AML), often necessary for leukemogenesis, persistent throughout the disease course, and highly leukemia specific, making them attractive neoantigen targets for immunotherapy. A nonameric peptide derived from a prevalent CBFB-MYH11 fusion protein was found to be immunogenic in HLA-B*40:01 + donors. High-avidity CD8 + T cell clones isolated from healthy donors killed CBFB-MYH11 + HLA-B*40:01 + AML cell lines and primary human AML samples in vitro. CBFB-MYH11–specific T cells also controlled CBFB-MYH11 + HLA-B*40:01 + AML in vivo in a patient-derived murine xenograft model. High-avidity CBFB-MYH11 epitope–specific T cell receptors (TCRs) transduced into CD8 + T cells conferred antileukemic activity in vitro. Our data indicate that the CBFB-MYH11 fusion neoantigen is naturally presented on AML blasts and enables T cell recognition and killing of AML. We provide proof of principle for immunologically targeting AML-initiating fusions and demonstrate that targeting neoantigens has clinical relevance even in low–mutational frequency cancers like fusion-driven AML. This work also represents a first critical step toward the development of TCR T cell immunotherapy targeting fusion gene–driven AML.

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          Most cited references11

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          Is Open Access

          NetMHCpan-3.0; improved prediction of binding to MHC class I molecules integrating information from multiple receptor and peptide length datasets

          Background Binding of peptides to MHC class I molecules (MHC-I) is essential for antigen presentation to cytotoxic T-cells. Results Here, we demonstrate how a simple alignment step allowing insertions and deletions in a pan-specific MHC-I binding machine-learning model enables combining information across both multiple MHC molecules and peptide lengths. This pan-allele/pan-length algorithm significantly outperforms state-of-the-art methods, and captures differences in the length profile of binders to different MHC molecules leading to increased accuracy for ligand identification. Using this model, we demonstrate that percentile ranks in contrast to affinity-based thresholds are optimal for ligand identification due to uniform sampling of the MHC space. Conclusions We have developed a neural network-based machine-learning algorithm leveraging information across multiple receptor specificities and ligand length scales, and demonstrated how this approach significantly improves the accuracy for prediction of peptide binding and identification of MHC ligands. The method is available at www.cbs.dtu.dk/services/NetMHCpan-3.0. Electronic supplementary material The online version of this article (doi:10.1186/s13073-016-0288-x) contains supplementary material, which is available to authorized users.
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            Immune epitope database analysis resource

            The immune epitope database analysis resource (IEDB-AR: http://tools.iedb.org) is a collection of tools for prediction and analysis of molecular targets of T- and B-cell immune responses (i.e. epitopes). Since its last publication in the NAR webserver issue in 2008, a new generation of peptide:MHC binding and T-cell epitope predictive tools have been added. As validated by different labs and in the first international competition for predicting peptide:MHC-I binding, their predictive performances have improved considerably. In addition, a new B-cell epitope prediction tool was added, and the homology mapping tool was updated to enable mapping of discontinuous epitopes onto 3D structures. Furthermore, to serve a wider range of users, the number of ways in which IEDB-AR can be accessed has been expanded. Specifically, the predictive tools can be programmatically accessed using a web interface and can also be downloaded as software packages.
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              NetMHC-3.0: accurate web accessible predictions of human, mouse and monkey MHC class I affinities for peptides of length 8–11

              NetMHC-3.0 is trained on a large number of quantitative peptide data using both affinity data from the Immune Epitope Database and Analysis Resource (IEDB) and elution data from SYFPEITHI. The method generates high-accuracy predictions of major histocompatibility complex (MHC): peptide binding. The predictions are based on artificial neural networks trained on data from 55 MHC alleles (43 Human and 12 non-human), and position-specific scoring matrices (PSSMs) for additional 67 HLA alleles. As only the MHC class I prediction server is available, predictions are possible for peptides of length 8–11 for all 122 alleles. artificial neural network predictions are given as actual IC50 values whereas PSSM predictions are given as a log-odds likelihood scores. The output is optionally available as download for easy post-processing. The training method underlying the server is the best available, and has been used to predict possible MHC-binding peptides in a series of pathogen viral proteomes including SARS, Influenza and HIV, resulting in an average of 75–80% confirmed MHC binders. Here, the performance is further validated and benchmarked using a large set of newly published affinity data, non-redundant to the training set. The server is free of use and available at: http://www.cbs.dtu.dk/services/NetMHC.
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                Author and article information

                Contributors
                (View ORCID Profile)
                Journal
                Journal of Clinical Investigation
                American Society for Clinical Investigation
                0021-9738
                1558-8238
                October 1 2020
                October 1 2020
                October 1 2020
                August 24 2020
                August 24 2020
                October 1 2020
                : 130
                : 10
                : 5127-5141
                Article
                10.1172/JCI137723
                7524498
                32831296
                6856018c-27a0-4749-b346-26566a7bc919
                © 2020
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