Large-scale validation of methods for cytotoxic T-lymphocyte epitope prediction

<p>Abstract</p> <p>Background</p> <p>Reliable predictions of Cytotoxic T lymphocyte (CTL) epitopes are essential for rational vaccine design. Most importantly, they can minimize the experimental effort needed to identify epitopes. NetCTL is a web-based tool designed for predicting human CTL epitopes in any given protein. It does so by integrating predictions of proteasomal cleavage, TAP transport efficiency, and MHC class I affinity. At least four other methods have been developed recently that likewise attempt to predict CTL epitopes: EpiJen, MAPPP, MHC-pathway, and WAPP. In order to compare the performance of prediction methods, objective benchmarks and standardized performance measures are needed. Here, we develop such large-scale benchmark and corresponding performance measures and report the performance of an updated version 1.2 of NetCTL in comparison with the four other methods.</p> <p>Results</p> <p>We define a number of performance measures that can handle the different types of output data from the five methods. We use two evaluation datasets consisting of known HIV CTL epitopes and their source proteins. The source proteins are split into all possible 9 mers and except for annotated epitopes; all other 9 mers are considered non-epitopes. In the RANK measure, we compare two methods at a time and count how often each of the methods rank the epitope highest. In another measure, we find the specificity of the methods at three predefined sensitivity values. Lastly, for each method, we calculate the percentage of known epitopes that rank within the 5% peptides with the highest predicted score.</p> <p>Conclusion</p> <p>NetCTL-1.2 is demonstrated to have a higher predictive performance than EpiJen, MAPPP, MHC-pathway, and WAPP on all performance measures. The higher performance of NetCTL-1.2 as compared to EpiJen and MHC-pathway is, however, not statistically significant on all measures. In the large-scale benchmark calculation consisting of 216 known HIV epitopes covering all 12 recognized HLA supertypes, the NetCTL-1.2 method was shown to have a sensitivity among the 5% top-scoring peptides above 0.72. On this dataset, the best of the other methods achieved a sensitivity of 0.64. The NetCTL-1.2 method is available at <url>http://www.cbs.dtu.dk/services/NetCTL</url>.</p> <p>All used datasets are available at <url>http://www.cbs.dtu.dk/suppl/immunology/CTL-1.2.php</url>.</p

NetMHCpan, a Method for Quantitative Predictions of Peptide Binding to Any HLA-A and -B Locus Protein of Known Sequence

Binding of peptides to Major Histocompatibility Complex (MHC) molecules is the single most selective step in the recognition of pathogens by the cellular immune system. The human MHC class I system (HLA-I) is extremely polymorphic. The number of registered HLA-I molecules has now surpassed 1500. Characterizing the specificity of each separately would be a major undertaking.Here, we have drawn on a large database of known peptide-HLA-I interactions to develop a bioinformatics method, which takes both peptide and HLA sequence information into account, and generates quantitative predictions of the affinity of any peptide-HLA-I interaction. Prospective experimental validation of peptides predicted to bind to previously untested HLA-I molecules, cross-validation, and retrospective prediction of known HIV immune epitopes and endogenous presented peptides, all successfully validate this method. We further demonstrate that the method can be applied to perform a clustering analysis of MHC specificities and suggest using this clustering to select particularly informative novel MHC molecules for future biochemical and functional analysis.Encompassing all HLA molecules, this high-throughput computational method lends itself to epitope searches that are not only genome- and pathogen-wide, but also HLA-wide. Thus, it offers a truly global analysis of immune responses supporting rational development of vaccines and immunotherapy. It also promises to provide new basic insights into HLA structure-function relationships. The method is available at http://www.cbs.dtu.dk/services/NetMHCpan

PepDist: A New Framework for Protein-Peptide Binding Prediction based on Learning Peptide Distance Functions

BACKGROUND: Many different aspects of cellular signalling, trafficking and targeting mechanisms are mediated by interactions between proteins and peptides. Representative examples are MHC-peptide complexes in the immune system. Developing computational methods for protein-peptide binding prediction is therefore an important task with applications to vaccine and drug design. METHODS: Previous learning approaches address the binding prediction problem using traditional margin based binary classifiers. In this paper we propose PepDist: a novel approach for predicting binding affinity. Our approach is based on learning peptide-peptide distance functions. Moreover, we suggest to learn a single peptide-peptide distance function over an entire family of proteins (e.g. MHC class I). This distance function can be used to compute the affinity of a novel peptide to any of the proteins in the given family. In order to learn these peptide-peptide distance functions, we formalize the problem as a semi-supervised learning problem with partial information in the form of equivalence constraints. Specifically, we propose to use DistBoost [1,2], which is a semi-supervised distance learning algorithm. RESULTS: We compare our method to various state-of-the-art binding prediction algorithms on MHC class I and MHC class II datasets. In almost all cases, our method outperforms all of its competitors. One of the major advantages of our novel approach is that it can also learn an affinity function over proteins for which only small amounts of labeled peptides exist. In these cases, our method's performance gain, when compared to other computational methods, is even more pronounced. We have recently uploaded the PepDist webserver which provides binding prediction of peptides to 35 different MHC class I alleles. The webserver which can be found at is powered by a prediction engine which was trained using the framework presented in this paper. CONCLUSION: The results obtained suggest that learning a single distance function over an entire family of proteins achieves higher prediction accuracy than learning a set of binary classifiers for each of the proteins separately. We also show the importance of obtaining information on experimentally determined non-binders. Learning with real non-binders generalizes better than learning with randomly generated peptides that are assumed to be non-binders. This suggests that information about non-binding peptides should also be published and made publicly available

Identification of a cyclin B1-derived CTL epitope eliciting spontaneous responses in both cancer patients and healthy donors

With the aim to identify cyclin B1-derived peptides with high affinity for HLA-A2, we used three in silico prediction algorithms to screen the protein sequence for possible HLA-A2 binders. One peptide scored highest in all three algorithms, and the high HLA-A2-binding affinity of this peptide was verified in an HLA stabilization assay. By stimulation with peptide-loaded dendritic cells a CTL clone was established, which was able to kill two breast cancer cell lines in an HLA-A2-dependent and peptide-specific manner, demonstrating presentation of the peptide on the surface of cancer cells. Furthermore, blood from cancer patients and healthy donors was screened for spontaneous T-cell reactivity against the peptide in IFN-γ ELISPOT assays. Patients with breast cancer, malignant melanoma, or renal cell carcinoma hosted powerful and high-frequency T-cell responses against the peptide. In addition, when blood from healthy donors was tested, similar responses were observed. Ultimately, serum from cancer patients and healthy donors was analyzed for anti-cyclin B1 antibodies. Humoral responses against cyclin B1 were frequently detected in both cancer patients and healthy donors. In conclusion, a high-affinity cyclin B1-derived HLA-A2-restricted CTL epitope was identified, which was presented on the cell surface of cancer cells, and elicited spontaneous T-cell responses in cancer patients and healthy donors

Identification of Lck-derived peptides applicable to anti-cancer vaccine for patients with human leukocyte antigen-A3 supertype alleles

The identification of peptide vaccine candidates to date has been focused on human leukocyte antigen (HLA)-A2 and -A24 alleles. In this study, we attempted to identify cytotoxic T lymphocyte (CTL)-directed Lck-derived peptides applicable to HLA-A11+, -A31+, or -A33+ cancer patients, because these HLA-A alleles share binding motifs, designated HLA-A3 supertype alleles, and because the Lck is preferentially expressed in metastatic cancer. Twenty-one Lck-derived peptides were prepared based on the binding motif to the HLA-A3 supertype alleles. They were first screened for their recognisability by immunoglobulin G (IgG) in the plasma of prostate cancer patients, and the selected candidates were subsequently tested for their potential to induce peptide-specific CTLs from peripheral blood mononuclear cells of HLA-A3 supertype+ cancer patients. As a result, four Lck peptides were frequently recognised by IgGs, and three of them – Lck90−99, Lck449−458, and Lck450−458 – efficiently induced peptide-specific and cancer-reactive CTLs. Their cytotoxicity towards cancer cells was mainly ascribed to HLA class I-restricted and peptide-specific CD8+ T cells. These results indicate that these three Lck peptides are applicable to HLA-A3 supertype+ cancer patients, especially those with metastasis. This information could facilitate the development of peptide-based anti-cancer vaccine for patients with alleles other than HLA-A2 and -A24

Modification of the carboxy-terminal flanking region of a universal influenza epitope alters CD4+ T-cell repertoire selection

Human CD4+ αβ T cells are activated via T-cell receptor recognition of peptide epitopes presented by major histocompatibility complex (MHC) class II (MHC-II). The open ends of the MHC-II binding groove allow peptide epitopes to extend beyond a central nonamer core region at both the amino- and carboxy-terminus. We have previously found that these non-bound C-terminal residues can alter T cell activation in an MHC allele-transcending fashion, although the mechanism for this effect remained unclear. Here we show that modification of the C-terminal peptide-flanking region of an influenza hemagglutinin (HA305−320) epitope can alter T-cell receptor binding affinity, T-cell activation and repertoire selection of influenza-specific CD4+ T cells expanded from peripheral blood. These data provide the first demonstration that changes in the C-terminus of the peptide-flanking region can substantially alter T-cell receptor binding affinity, and indicate a mechanism through which peptide flanking residues could influence repertoire selection

Peptide microarrays for the profiling of cytotoxic T-lymphocyte activity using minimum numbers of cells

The identification of epitopes that elicit cytotoxic T-lymphocyte activity is a prerequisite for the development of cancer-specific immunotherapies. However, especially the parallel characterization of several epitopes is limited by the availability of T cells. Microarrays have enabled an unprecedented miniaturization and parallelization in biological assays. Here, we developed peptide microarrays for the detection of CTL activity. MHC class I-binding peptide epitopes were pipetted onto polymer-coated glass slides. Target cells, loaded with the cell-impermeant dye calcein, were incubated on these arrays, followed by incubation with antigen-expanded CTLs. Cytotoxic activity was detected by release of calcein and detachment of target cells. With only 200,000 cells per microarray, CTLs could be detected at a frequency of 0.5% corresponding to 1,000 antigen-specific T cells. Target cells and CTLs only settled on peptide spots enabling a clear separation of individual epitopes. Even though no physical boundaries were present between the individual spots, peptide loading only occurred locally and cytolytic activity was confined to the spots carrying the specific epitope. The peptide microarrays provide a robust platform that implements the whole process from antigen presentation to the detection of CTL activity in a miniaturized format. The method surpasses all established methods in the minimum numbers of cells required. With antigen uptake occurring on the microarray, further applications are foreseen in the testing of antigen precursors that require uptake and processing prior to presentation

HER2-based recombinant immunogen to target DCs through FcγRs for cancer immunotherapy

Dendritic cell (DC)-based immunotherapy is an attractive approach to induce long lasting antitumor effector cells aiming to control cancer progression. DC targeting is a critical step in the design of DC vaccines in order to optimize delivery and processing of the antigen, and several receptors have been characterized for this purpose. In this study, we employed the FcγRs to target DCs both in vitro and in vivo. We designed a recombinant molecule (HER2-Fc) composed of the immunogenic sequence of the human tumor-associated antigen HER2 (aa 364–391) and the Fc domain of a human IgG1. In a mouse model, HER2-Fc cDNA vaccination activated significant T cell-mediated immune responses towards HER2 peptide epitopes as detected by IFN-γ ELIspot and induced longer tumor latency as compared to Ctrl-Fc-vaccinated control mice. Human in vitro studies indicated that the recombinant HER2-Fc immunogen efficiently targeted human DCs through the FcγRs resulting in protein cross-processing and in the activation of autologous HER2-specific CD8+ T cells from breast cancer patients

Natural CD4+ T-Cell Responses against Indoleamine 2,3-Dioxygenase

The enzyme indoleamine 2,3-dioxygenase (IDO) contributes to immune tolerance in a variety of settings. In cancer IDO is expressed within the tumor itself as well as in antigen-presenting cells in tumor-draining lymph nodes, where it endorses the establishment of peripheral immune tolerance to tumor antigens. Recently, we described cytotoxic CD8(+) T-cell reactivity towards IDO-derived peptides.In the present study, we show that CD4(+) helper T cells additionally spontaneously recognize IDO. Hence, we scrutinized the vicinity of the previously described HLA-A*0201-restricted IDO-epitope for CD4(+) T-cell epitopes. We demonstrated the presence of naturally occurring IDO-specific CD4(+) T cells in cancer patients and to a lesser extent in healthy donors by cytokine release ELISPOT. IDO-reactive CD4(+) T cells released IFN-γ, TNF-α, as well as IL-17. We confirm HLA class II-restriction by the addition of HLA class II specific blocking antibodies. In addition, we detected a trend between class I- and class II-restricted IDO responses and detected an association between IDO-specific CD4(+) T cells and CD8(+) CMV-responses. Finally, we could detect IL-10 releasing IDO-reactive CD4(+) T cells.IDO is spontaneously recognized by HLA class II-restricted, CD4(+) T cells in cancer patients and in healthy individuals. IDO-specific T cells may participate in immune-regulatory networks where the activation of pro-inflammatory IDO-specific CD4(+) responses may well overcome or delay the immune suppressive actions of the IDO-protein, which are otherwise a consequence of the early expression of IDO in maturing antigen presenting cells. In contrast, IDO-specific regulatory T cells may enhance IDO-mediated immune suppression