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      Differential Ion Mobility Spectrometry Coupled to Tandem Mass Spectrometry Enables Targeted Leukemia Antigen Detection

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          Abstract

          Differential ion mobility spectrometry (DIMS) can be used as a filter to remove undesired background ions from reaching the mass spectrometer. The ability to use DIMS as a filter for known analytes makes DIMS coupled to tandem mass spectrometry (DIMS–MS/MS) a promising technique for the detection of cancer antigens that can be predicted by computational algorithms. In experiments using DIMS–MS/MS that were performed without the use of high-performance liquid chromatography (HPLC), a predicted model antigen, GLR (FLSSANEHL), was detected at a concentration of 10 pM (20 amol) in a mixture containing 94 competing model peptide antigens, each at a concentration of 1 μM. Without DIMS filtering, the GLR peptide was undetectable in the mixture even at 100 nM. Again, without using HPLC, DIMS–MS/MS was used to detect 2 of 3 previously characterized antigens produced by the leukemia cell line U937.A2. Because of its sensitivity, a targeted DIMS–MS/MS methodology can likely be used to probe for predicted cancer antigens from cancer cell lines as well as human tumor samples.

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          Most cited references31

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          Reliable prediction of T-cell epitopes using neural networks with novel sequence representations.

          In this paper we describe an improved neural network method to predict T-cell class I epitopes. A novel input representation has been developed consisting of a combination of sparse encoding, Blosum encoding, and input derived from hidden Markov models. We demonstrate that the combination of several neural networks derived using different sequence-encoding schemes has a performance superior to neural networks derived using a single sequence-encoding scheme. The new method is shown to have a performance that is substantially higher than that of other methods. By use of mutual information calculations we show that peptides that bind to the HLA A*0204 complex display signal of higher order sequence correlations. Neural networks are ideally suited to integrate such higher order correlations when predicting the binding affinity. It is this feature combined with the use of several neural networks derived from different and novel sequence-encoding schemes and the ability of the neural network to be trained on data consisting of continuous binding affinities that gives the new method an improved performance. The difference in predictive performance between the neural network methods and that of the matrix-driven methods is found to be most significant for peptides that bind strongly to the HLA molecule, confirming that the signal of higher order sequence correlation is most strongly present in high-binding peptides. Finally, we use the method to predict T-cell epitopes for the genome of hepatitis C virus and discuss possible applications of the prediction method to guide the process of rational vaccine design.
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            Cardiovascular toxicity and titin cross-reactivity of affinity-enhanced T cells in myeloma and melanoma.

            An obstacle to cancer immunotherapy has been that the affinity of T-cell receptors (TCRs) for antigens expressed in tumors is generally low. We initiated clinical testing of engineered T cells expressing an affinity-enhanced TCR against HLA-A*01-restricted MAGE-A3. Open-label protocols to test the TCRs for patients with myeloma and melanoma were initiated. The first two treated patients developed cardiogenic shock and died within a few days of T-cell infusion, events not predicted by preclinical studies of the high-affinity TCRs. Gross findings at autopsy revealed severe myocardial damage, and histopathological analysis revealed T-cell infiltration. No MAGE-A3 expression was detected in heart autopsy tissues. Robust proliferation of the engineered T cells in vivo was documented in both patients. A beating cardiomyocyte culture generated from induced pluripotent stem cells triggered T-cell killing, which was due to recognition of an unrelated peptide derived from the striated muscle-specific protein titin. These patients demonstrate that TCR-engineered T cells can have serious and not readily predictable off-target and organ-specific toxicities and highlight the need for improved methods to define the specificity of engineered TCRs.
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              SYFPEITHI: database for MHC ligands and peptide motifs.

              The first version of the major histocompatibility complex (MHC) databank SYFPEITHI: database for MHC ligands and peptide motifs, is now available to the general public. It contains a collection of MHC class I and class II ligands and peptide motifs of humans and other species, such as apes, cattle, chicken, and mouse, for example, and is continuously updated. All motifs currently available are accessible as individual entries. Searches for MHC alleles, MHC motifs, natural ligands, T-cell epitopes, source proteins/organisms and references are possible. Hyperlinks to the EMBL and PubMed databases are included. In addition, ligand predictions are available for a number of MHC allelic products. The database content is restricted to published data only.
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                Author and article information

                Journal
                J Proteome Res
                J. Proteome Res
                pr
                jprobs
                Journal of Proteome Research
                American Chemical Society
                1535-3893
                1535-3907
                03 September 2015
                03 September 2014
                03 October 2014
                : 13
                : 10
                : 4356-4362
                Affiliations
                []Lineberger Comprehensive Cancer Center, University of North Carolina at Chapel Hill , 450 West Drive, 21-244, Chapel Hill, North Carolina 27599, United States
                []Department of Chemistry, University of North Carolina at Chapel Hill , 320 Caudill Hall, Chapel Hill, North Carolina 27599, United States
                Author notes
                [* ](P.M.A.) E-mail: parmiste@ 123456med.unc.edu .
                Article
                10.1021/pr500527c
                4184456
                25184817
                e8975663-a7f9-4058-8090-5ee44fdfbcd2
                Copyright © 2014 American Chemical Society

                Terms of Use

                History
                : 28 May 2014
                Funding
                National Institutes of Health, United States
                Categories
                Article
                Custom metadata
                pr500527c
                pr-2014-00527c

                Molecular biology
                differential ion mobility spectrometry,dims,faims,mass spectrometry,cancer antigen

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