A novel immunization approach for dengue infection based on conserved T cell epitopes formulated in calcium phosphate nanoparticles.

Dengue virus (DV) is the etiologic agent of dengue fever, the most significant mosquito-borne viral disease in humans. Most DV vaccine approaches are focused on generating antibody mediated responses; one such DV vaccine is approved for use in humans but its efficacy is limited. While it is clear that T cell responses play important role in DV infection and subsequent disease manifestations, fewer studies are aimed at developing vaccines that induce robust T cells responses. Potent T cell based vaccines require 2 critical components: the identification of specific T cell stimulating MHC associated peptides, and an optimized vaccine delivery vehicle capable of simultaneously delivering the antigens and any required adjuvants. We have previously identified and characterized DV specific HLA-A2 and -A24 binding DV serotypes conserved epitopes, and the feasibility of an epitope based vaccine for DV infection. In this study, we build on those previous studies and describe an investigational DV vaccine using T cell epitopes incorporated into a calcium phosphate nanoparticle (CaPNP) delivery system. This study presents a comprehensive analysis of functional immunogenicity of DV CaPNP/multipeptide formulations in vitro and in vivo and demonstrates the CaPNP/multipeptide vaccine is capable of inducing T cell responses against all 4 serotypes of DV. This synthetic vaccine is also cost effective, straightforward to manufacture, and stable at room temperature in a lyophilized form. This formulation may serve as an effective candidate DV vaccine that protects against all 4 serotypes as either a prophylactic or therapeutic vaccine.

T-cell Receptors for Clinical Therapy: In Vitro Assessment of Toxicity Risk.

Adoptive therapy with T-cell receptor (TCR)-engineered T cells has shown promising results in the treatment of patients with tumors, and the number of TCRs amenable for clinical testing is expanding rapidly. Notably, adoptive therapy with T cells is challenged by treatment-related side effects, which calls for cautious selection of target antigens and TCRs that goes beyond their mere ability to induce high T-cell reactivity. Here, we propose a sequence of in vitro assays to improve selection of TCRs and exemplify risk assessments of on-target as well as off-target toxicities using TCRs directed against cancer germline antigens. The proposed panel of assays covers parameters considered key to safety, such as expression of target antigen in healthy tissues, determination of a TCR's recognition motif toward its cognate peptide, and a TCR's cross-reactivity toward noncognate peptides. Clin Cancer Res; 23(20); 6012-20. ©2017 AACR.

Evaluation of MHC-II peptide binding prediction servers: applications for vaccine research.

BACKGROUND: Initiation and regulation of immune responses in humans involves recognition of peptides presented by human leukocyte antigen class II (HLA-II) molecules. These peptides (HLA-II T-cell epitopes) are increasingly important as research targets for the development of vaccines and immunotherapies. HLA-II peptide binding studies involve multiple overlapping peptides spanning individual antigens, as well as complete viral proteomes. Antigen variation in pathogens and tumor antigens, and extensive polymorphism of HLA molecules increase the number of targets for screening studies. Experimental screening methods are expensive and time consuming and reagents are not readily available for many of the HLA class II molecules. Computational prediction methods complement experimental studies, minimize the number of validation experiments, and significantly speed up the epitope mapping process. We collected test data from four independent studies that involved 721 peptide binding assays. Full overlapping studies of four antigens identified binding affinity of 103 peptides to seven common HLA-DR molecules (DRB1*0101, 0301, 0401, 0701, 1101, 1301, and 1501). We used these data to analyze performance of 21 HLA-II binding prediction servers accessible through the WWW. RESULTS: Because not all servers have predictors for all tested HLA-II molecules, we assessed a total of 113 predictors. The length of test peptides ranged from 15 to 19 amino acids. We tried three prediction strategies - the best 9-mer within the longer peptide, the average of best three 9-mer predictions, and the average of all 9-mer predictions within the longer peptide. The best strategy was the identification of a single best 9-mer within the longer peptide. Overall, measured by the receiver operating characteristic method (AROC), 17 predictors showed good (AROC > 0.8), 41 showed marginal (AROC > 0.7), and 55 showed poor performance (AROC < 0.7). Good performance predictors included HLA-DRB1*0101 (seven), 1101 (six), 0401 (three), and 0701 (one). The best individual predictor was NETMHCIIPAN, closely followed by PROPRED, IEDB (Consensus), and MULTIPRED (SVM). None of the individual predictors was shown to be suitable for prediction of promiscuous peptides. Current predictive capabilities allow prediction of only 50% of actual T-cell epitopes using practical thresholds. CONCLUSION: The available HLA-II servers do not match prediction capabilities of HLA-I predictors. Currently available HLA-II prediction servers offer only a limited prediction accuracy and the development of improved predictors is needed for large-scale studies, such as proteome-wide epitope mapping. The requirements for accuracy of HLA-II binding predictions are stringent because of the substantial effect of false positives.

Escape from HLA-B*08-restricted CD8 T cells by hepatitis C virus is associated with fitness costs.

The inherent sequence diversity of the hepatitis C virus (HCV) represents a major hurdle for the adaptive immune system to control viral replication. Mutational escape within targeted CD8 epitopes during acute HCV infection has been well documented and is one possible mechanism for T-cell failure. HLA-B*08 was recently identified as one HLA class I allele associated with spontaneous clearance of HCV replication. Selection of escape mutations in the immunodominant HLA-B*08-restricted epitope HSKKKCDEL(1395-1403) was observed during acute infection. However, little is known about the impact of escape mutations in this epitope on viral replication capacity. Their previously reported reversion back toward the consensus residue in patients who do not possess the B*08 allele suggests that the consensus sequence in this epitope is advantageous for viral replication in the absence of immune pressure. The aim of this study was to determine the impact of mutational escape from this immunodominant epitope on viral replication. We analyzed it with a patient cohort with chronic HCV genotype 1b infection and in a single-source outbreak (genotype 1b). Sequence changes in this highly conserved region are rare and selected almost exclusively in the presence of the HLA-B*08 allele. When tested in the subgenomic replicon (Con1), the observed mutations reduce viral replication compared with the prototype sequence. The results provide direct evidence that escape mutations in this epitope are associated with fitness costs and that the antiviral effect of HLA-B*08-restricted T cells is sufficiently strong to force the virus to adopt a relatively unfavorable sequence.

MULTIPRED: a computational system for prediction of promiscuous HLA binding peptides.

MULTIPRED is a web-based computational system for the prediction of peptide binding to multiple molecules (proteins) belonging to human leukocyte antigens (HLA) class I A2, A3 and class II DR supertypes. It uses hidden Markov models and artificial neural network methods as predictive engines. A novel data representation method enables MULTIPRED to predict peptides that promiscuously bind multiple HLA alleles within one HLA supertype. Extensive testing was performed for validation of the prediction models. Testing results show that MULTIPRED is both sensitive and specific and it has good predictive ability (area under the receiver operating characteristic curve A(ROC) > 0.80). MULTIPRED can be used for the mapping of promiscuous T-cell epitopes as well as the regions of high concentration of these targets--termed T-cell epitope hotspots. MULTIPRED is available at http://antigen.i2r.a-star.edu.sg/multipred/.

Fitness costs limit viral escape from cytotoxic T lymphocytes at a structurally constrained epitope.

The intense selection pressure exerted by virus-specific cytotoxic T lymphocytes (CTL) on replicating human immunodeficiency virus and simian immunodeficiency virus results in the accumulation of CTL epitope mutations. It has been assumed that fitness costs can limit the evolution of CTL epitope mutations. However, only a limited number of studies have carefully examined this possibility. To explore the fitness costs associated with viral escape from p11C, C-M-specific CTL, we constructed a panel of viruses encoding point mutations at each position of the entire p11C, C-M epitope. Amino acid substitutions at positions 3, 4, 5, 6, 7, and 9 of the epitope significantly impaired virus replication by altering virus production and Gag protein expression as well as by destabilizing mature cores. Amino acid substitutions at position 2 of the epitope were tolerated but required reversion or additional compensatory mutations to generate replication-competent viruses. Finally, while amino acid substitutions at positions 1 and 8 of the p11C, C-M epitope were functionally tolerated, these substitutions were recognized by p11C, C-M-specific CTL and therefore provided no selection advantage for the virus. Together, these data suggest that limited sequence variation is tolerated by the region of the capsid encoding the p11C, C-M epitope and therefore that only a very limited number of mutations can allow successful viral escape from the p11C, C-M-specific CTL response.

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.