Epitope Prediction Methods Research Articles

Antibody Class(es) Predictor for Epitopes (AbCPE): A Multi-Label Classification Algorithm.

Development of vaccines and therapeutic antibodies to deal with infectious and other diseases are the most perceptible scientific interventions that have had huge impact on public health including that in the current Covid-19 pandemic. From inactivation methodologies to reverse vaccinology, vaccine development strategies of 21st century have undergone several transformations and are moving towards rational design approaches. These developments are driven by data as the combinatorials involved in antigenic diversity of pathogens and immune repertoire of hosts are enormous. The computational prediction of epitopes is central to these developments and numerous B-cell epitope prediction methods developed over the years in the field of immunoinformatics have contributed enormously. Most of these methods predict epitopes that could potentially bind to an antibody regardless of its type and only a few account for antibody class specific epitope prediction. Recent studies have provided evidence of more than one class of antibodies being associated with a particular disease. Therefore, it is desirable to predict and prioritize ‘peptidome’ representing B-cell epitopes that can potentially bind to multiple classes of antibodies, as an open problem in immunoinformatics. To address this, AbCPE, a novel algorithm based on multi-label classification approach has been developed for prediction of antibody class(es) to which an epitope can potentially bind. The epitopes binding to one or more antibody classes (IgG, IgE, IgA and IgM) have been used as a knowledgebase to derive features for prediction. Multi-label algorithms, Binary Relevance and Label Powerset were applied along with Random Forest and AdaBoost. Classifier performance was assessed using evaluation measures like Hamming Loss, Precision, Recall and F1 score. The Binary Relevance model based on dipeptide composition, Random Forest and AdaBoost achieved the best results with Hamming Loss of 0.1121 and 0.1074 on training and test sets respectively. The results obtained by AbCPE are promising. To the best of our knowledge, this is the first multi-label method developed for prediction of antibody class(es) for sequential B-cell epitopes and is expected to bring a paradigm shift in the field of immunoinformatics and immunotherapeutic developments in synthetic biology. The AbCPE web server is available at http://bioinfo.unipune.ac.in/AbCPE/Home.html.

Abstract SY22-02: Personalized cancer vaccines: Antigen selection and translation into the clinic

Abstract Personalized cancer vaccination targets specific cancer mutations of the individual patient's tumor, also referred to as neoantigens. Such “bespoke” vaccines are now being tested in multiple cancers and in combination with checkpoint inhibitors. This session will discuss our computational methods for antigenic epitope prediction, antigen selection (personalized and shared neoantigens derived from frame-shift mutations), validation in vitro and in vivo and future directions for the field. Citation Format: Nina Bhardwaj. Personalized cancer vaccines: Antigen selection and translation into the clinic [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr SY22-02.

Epitopes of Protein Binders Are Related to the Structural Flexibility of a Target Protein Surface.

Protein binders including antibodies are known not to bind to random sites of target proteins, and their functional effectiveness mainly depends on the binding region, called the epitope. For the development of protein binders with desired functions, it is thus critical to understand which surface region protein binders prefer (or do not prefer) to bind. The current methods for epitope prediction focus on static indicators such as structural geometry or amino acid propensity, whereas protein binding events are in fact a consequence of dynamic interactions. Here, we demonstrate that the preference for a binding site by protein binders is strongly related to the structural flexibility of a target protein surface. Molecular dynamics simulations on unbound forms of antigen structures revealed that the antigen surface in direct contact with antibodies is less flexible than the rest of the surface. This tendency was shown to be similar in other non-antibody protein binders such as affibody, DARPin, monobody, and repebody. We also found that the relatedness of epitopes to the structural flexibility of a target protein surface is dependent on the secondary structure elements of paratopes. Monobody and repebody, whose binding sites are composed of β-strands, distinctively prefer to bind to a relatively more rigid region of a target protein. These observations enabled us to develop a simple epitope prediction method which shows a comparable performance to the commonly used ones.

Linear B-Cell Epitope Prediction for In Silico Vaccine Design: A Performance Review of Methods Available via Command-Line Interface.

Linear B-cell epitope prediction research has received a steadily growing interest ever since the first method was developed in 1981. B-cell epitope identification with the help of an accurate prediction method can lead to an overall faster and cheaper vaccine design process, a crucial necessity in the COVID-19 era. Consequently, several B-cell epitope prediction methods have been developed over the past few decades, but without significant success. In this study, we review the current performance and methodology of some of the most widely used linear B-cell epitope predictors which are available via a command-line interface, namely, BcePred, BepiPred, ABCpred, COBEpro, SVMTriP, LBtope, and LBEEP. Additionally, we attempted to remedy performance issues of the individual methods by developing a consensus classifier, which combines the separate predictions of these methods into a single output, accelerating the epitope-based vaccine design. While the method comparison was performed with some necessary caveats and individual methods might perform much better for specialized datasets, we hope that this update in performance can aid researchers towards the choice of a predictor, for the development of biomedical applications such as designed vaccines, diagnostic kits, immunotherapeutics, immunodiagnostic tests, antibody production, and disease diagnosis and therapy.

In silico detection of SARS-CoV-2 specific B-cell epitopes and validation in ELISA for serological diagnosis of COVID-19

Rapid generation of diagnostics is paramount to understand epidemiology and to control the spread of emerging infectious diseases such as COVID-19. Computational methods to predict serodiagnostic epitopes that are specific for the pathogen could help accelerate the development of new diagnostics. A systematic survey of 27 SARS-CoV-2 proteins was conducted to assess whether existing B-cell epitope prediction methods, combined with comprehensive mining of sequence databases and structural data, could predict whether a particular protein would be suitable for serodiagnosis. Nine of the predictions were validated with recombinant SARS-CoV-2 proteins in the ELISA format using plasma and sera from patients with SARS-CoV-2 infection, and a further 11 predictions were compared to the recent literature. Results appeared to be in agreement with 12 of the predictions, in disagreement with 3, while a further 5 were deemed inconclusive. We showed that two of our top five candidates, the N-terminal fragment of the nucleoprotein and the receptor-binding domain of the spike protein, have the highest sensitivity and specificity and signal-to-noise ratio for detecting COVID-19 sera/plasma by ELISA. Mixing the two antigens together for coating ELISA plates led to a sensitivity of 94% (N = 80 samples from persons with RT-PCR confirmed SARS-CoV-2 infection), and a specificity of 97.2% (N = 106 control samples).

Glycan Cluster Shielding and Antibody Epitopes on Lassa Virus Envelop Protein.

An understanding of how an antiviral monoclonal antibody recognizes its target is vital for the development of neutralizing antibodies and vaccines. The extensive glycosylation of viral proteins almost certainly affects the antibody response, but the investigation of such effects is hampered by the huge range of structures and interactions of surface glycans through their inherent complexity and flexibility. Here, we built an atomistic model of a fully glycosylated envelope protein complex of the Lassa virus and performed molecular dynamics simulations to characterize the impact of surface glycans on the antibody response. The simulations attested to the variety of conformations and interactions of surface glycans. The results show that glycosylation nonuniformly shields the surface of the complex and only marginally affects protein dynamics. The glycans gather in distinct clusters through interaction with protein residues, and only a few regions are left accessible by an antibody. We successfully recovered known protein epitopes by integrating the simulation results with existing sequence- and structure-based epitope prediction methods. The results emphasize the rich structural environment of glycans and demonstrate that shielding is not merely envelopment by a uniform blanket of sugars. This work provides a molecular basis for integrating otherwise elusive structural properties of glycans into vaccine and neutralizing antibody developments.

Delineating Novel Therapeutic Drug and Vaccine Targets for Staphylococcus cornubiensis NW1T Through Computational Analysis

Staphylococcal multidrug resistance is an emerging future threat. Among staphylococci species, Staphylococcus intermedius group (SIG) comprises of coagulase-positive four staphylococci isolated from veterinary wounds and post-operative infections in humans and animals. Staphylococcus cornubiensis has been reported as a new member of this group and is associated with infections in a wide range of wild and domesticated animals as well as in humans. Cases of the antibiotic resistance in different members of the SIG are reported indicating its future threat for antibiotic therapy. The objective of this study was to determine the unique potential drug and vaccine targets for infectious S. cornubiensis NW1T. Genome-wide subtractive proteomic approach accompanied by immunoinformatics analysis was performed on the newly reported genome of S. cornubiensis NW1T. Applying a stringent comparative proteomic analyses, 35 proteins were identified as novel druggable targets based on their function in pathogen specific metabolic pathways as well as their druggablity potential. Using antigenicity and epitope prediction methods, the possible B and T-cells antigenic peptides of these pathogen-specific essential proteins were determined. As a result, six proteins were prioritized as potential vaccine candidates that fulfilled the basic criteria of effective vaccine targets. During analyses, candidate proteins of S. cornubiensis were found to carry T-cell and B-cell epitopes and predicted to interact with both class I and II MHC molecules at significantly lower IC50 values. Moreover, a leading antigenic peptide “LVDTLNAAG” from penicillin binding protein of this pathogenic bacteria was found to interact with multiple MHC alleles of class I and II with IC50 value of < 100 nM. Molecular docking showed favorable interactions between this lead antigenic peptide with human HLA-C*05:01 cell surface receptor. This peptide is predicted as a promising epitope capable of triggering the immune response against pathogenic S. cornubiensis NW1T. The candidate proteins and epitopes determined in this study may be promising novel drug and vaccine targets against infection-causing S. cornubiensis.

Computational Prediction and Validation of Tumor-Associated Neoantigens.

Tumor progression is typically accompanied by an accumulation of driver and passenger somatic mutations. A handful of those mutations occur in protein coding genes which introduce non-synonymous polymorphisms. Certain substitutions may give rise to novel, tumor-associated antigens or neoantigens, presentable by cancer cells to the host adaptive immune system. As antigen recognition is the core of an effective immune response, the identification of patient tumor specific antigens derived from transformed cells is of importance for immunotherapeutic approaches. Recent technological advances in DNA sequencing of tumor genomes, advances in gene expression analysis, algorithm development for antigen predictions and methods for T-cell receptor (TCR) repertoire sequencing have facilitated the selection of candidate immunogenic neoantigens. In this regard, multiple research groups have reported encouraging results of neoantigen-based cancer vaccines that generate tumor antigen specific immune responses, both in mouse models and clinical trials. Additionally, both the quantity and quality of neoantigens has been shown to have predictive value for clinical outcomes in checkpoint-blockade immunotherapy in certain tumor types. Neoantigen recognition by vaccination or through adoptive T cell therapy may have unprecedented potential to advance cancer immunotherapy in combination with other approaches. In our review we discuss three parameters regarding neoantigens: computational methods for epitope prediction, experimental methods for epitope immunogenicity validation and future directions for improvement of those methods. Within each section, we will describe the advantages and limitations of existing methods as well as highlight pressing fundamental problems to be addressed.

Identification of similar epitopes between severe acute respiratory syndrome coronavirus-2 and Bacillus Calmette-Guérin: potential for cross-reactive adaptive immunity.

ObjectivesBacillus Calmette–Guérin (BCG) vaccination has been implicated in protection against severe acute respiratory syndrome coronavirus‐2 (SARS‐CoV‐2) and as a non‐specific immunisation method against the virus. We therefore decided to investigate T‐cell and B‐cell epitopes within the BCG‐Pasteur strain proteome for similarity to immunogenic peptides of SARS‐CoV‐2.MethodsWe used NetMHC 4.0 and BepiPred 2.0 epitope prediction methods for the analysis of the BCG‐Pasteur proteome to identify similar peptides to established and novel SARS‐CoV‐2 T‐cell and B‐cell epitopes.ResultsWe found 112 BCG MHC‐I‐restricted T‐cell epitopes similar to MHC‐I‐restricted T‐cell SARS‐CoV‐2 epitopes and 690 BCG B‐cell epitopes similar to SARS‐CoV‐2 B‐cell epitopes. The SARS‐CoV‐2 T‐cell epitopes represented 16 SARS‐CoV‐2 proteins, and the SARS‐CoV‐2 B‐cell epitopes represented 5 SARS‐CoV‐2 proteins, including the receptor binding domain of the spike glycoprotein.ConclusionAltogether, our results provide a mechanistic basis for the potential cross‐reactive adaptive immunity that may exist between the two microorganisms.

Computational design of a new chimeric protein for sero-diagnosis of Mycoplasma hominis

BACKGROUND: Development of sensitive and specific immunoassays for indirect detection of Mycoplasma hominis, an opportunistic bacterium in human genital tracts, will ideally use conserved, specific and immunoreactive antigen(s). Because of a high rate of sequence polymorphism, it is necessary to recognize and select conserved epitopes from bacterial surface proteins. this study was conducted to predict and evaluate appropriate epitopes of two membrane proteins, p120 and p80, by bioinformatics tools. MethodS: the linear epitopes of p120 and p80 were predicted by the Kolaskar and tongaonkar antigenicity method. After excluding non-specific epitopes, the remaining peptides were further analyzed by the BepiPred Linear Epitope Prediction method. In addition, the peptides were evaluated for other parameters including conservation, flexibility, hydrophilicity and immune system accessibility. RESULTS: From 26 primary predicted epitopes, 15 were recognized as specific peptides and further evaluation led to selection of 8 for the construction of a chimeric protein. conclUSionS: there are several bioinformatics tools for prediction of B-cell epitopes using different algorithms. Combining different approaches and tools can improve the reliability of the final results. The chimeric protein designed based on consensus predicted epitopes holds promise for the diagnosis of M. hominis infection. © 2018 edizioni Minerva Medica.

Benchmark datasets of immune receptor-epitope structural complexes

BackgroundThe development of accurate epitope prediction tools is important in facilitating disease diagnostics, treatment and vaccine development. The advent of new approaches making use of antibody and TCR sequence information to predict receptor-specific epitopes have the potential to transform the epitope prediction field. Development and validation of these new generation of epitope prediction methods would benefit from regularly updated high-quality receptor-antigen complex datasets.ResultsTo address the need for high-quality datasets to benchmark performance of these new generation of receptor-specific epitope prediction tools, a webserver called SCEptRe (Structural Complexes of Epitope-Receptor) was created. SCEptRe extracts weekly updated 3D complexes of antibody-antigen, TCR-pMHC and MHC-ligand from the Immune Epitope Database and clusters them based on antigen, receptor and epitope features to generate benchmark datasets. SCEptRe also provides annotated information such as CDR sequences and VDJ genes on the receptors. Users can generate custom datasets based by selecting thresholds for structural quality and clustering parameters (e.g. resolution, R-free factor, antigen or epitope sequence identity) based on their need.ConclusionsSCEptRe provides weekly updated, user-customized comprehensive benchmark datasets of immune receptor-epitope structural complexes. These datasets can be used to develop and benchmark performance of receptor-specific epitope prediction tools in the future. SCEptRe is freely accessible at http://tools.iedb.org/sceptre.

Functional Exposed Amino Acids of CarO Analysis as a Potential Vaccine Candidate in Acinetobacter Baumannii

Acinetobacter baumannii is a well-recognized cause of nosocomial infections. This organism is recognized to be among the most difficult antimicrobial-resistant gram-negative bacilli to control and treat. One of the main challenges we face is Carbapenem resistance in A. baumannii. Carbapenem resistance in A. baumannii associated with the loss of an outer membrane protein designated CarO (Carbapenem resistance outer membrane protein). This protein is a membrane porin of A. baumannii. Using specific antibodies against this protein exert a bacteriostatic or bactericidal effect in vitro. Attempts should be made to discover peptides that could mimic protein epitopes and possess the same immunogenicity as the complete protein. Subsequently, bioinformatics methods for epitope prediction have been developed leading to synthesis of such peptides that are important for development of vaccine. This study provides a basis for the design of pathogen specifically, B cell epitope-based vaccine that is targeted to diseases caused by A. baumannii in the global human population. A combination of available bioinformatics tools are used to understand and characterize the Baumannii Acinetobactin utilization structure of A. baumannii and appropriate selection regions as effective B cell epitopes and functional exposed amino acids. In conclusion, amino acids 19–158 were selected as vaccine candidate.

SPADE web service for prediction of allergen IgE epitopes.

The specific interaction of allergens with IgE antibodies and the allergen mediated cross-linking of receptor-bound IgE are key events of allergic diseases. The elucidation of the IgE binding sites (the epitopes) on the allergen surface is an important goal of allergy research. Only few allergen-specific IgE epitopes have been determined experimentally to date. Epitope prediction methods represent a viable alternative to experimental methods and have worked well with linear epitopes. However, as most IgE epitopes are of conformational and/or discontinuous nature sequence based prediction methods have had limited success in these cases. Here, we present the web server of the program SPADE (https://spade.uni-graz.at), which is the server implementation of a previously published program (1). In this approach we utilize the structural homology of cross-reactive allergens combined with the immunological cross-reactivity data for the discrimination of putative IgE-binding sites from non-cross-reactive surface patches. The method, although predictive, does not rely on machine-learning algorithms and does not require training data. The SPADE server features an easy-to-use interface, an automated pipeline consisting of third-party, as well as own, newly developed routines and a comprehensive output page.

Abstract B093: Development of prediction software PrDx, trained on peptide-MHC stability assays, shows new important positions in the binding patterns of the peptides-MHC I and II complexes

Abstract Mutations in cancer cells may lead to the formation of neo-epitopes potentially presented by both major histocompatibility complex (MHC) class I or II. These neo-epitopes may be recognized by CD8+ or CD4+ T-cells, and trigger an immune response. Only a small fraction of the neoepitopes will be displayed by the MHC class I or II. One of the challenges of cancer immunotherapy is therefore to predict which neoepitopes are susceptible to elicit a T-cell response. Software tools such as netMHC, MHC Flury and many others are over-predictive as the bulk part of the data used to train these methods are based on affinity assays. Several publications have indicated that stability assays may provide data that better correlate with epitope presentation by MHC. Prediction tools trained on stability assays may therefore be better at selecting neoepitopes resulting in more effective cancer vaccine design. We performed stability assay measurements for 10 MHC class I and 10 MHC class II alleles using a peptide scan library approach. In brief, random 9-mers where one position is known were used to measure stability of the peptide MHC complex. Next, the data were used to train a prediction tool, PrDx, that relies on a combination of different machine learning methods (random forest, feed forward neural networks and recurrent neural networks), of which the outputs are gathered in an assemble model. The model was then further trained with peptides predicted to bind with high stability, to the MHC alleles, until satisfactory performances were attained. To our surprise, PrDx showed new binding patterns for the alleles we trained. Although mostly similar to the binding patterns seen with affinity data trained method, the stability trained method is able to show new important positions in the binding patterns of the peptide-MHC complexes. Through retrospective analysis, our method seems able to select more accurately peptides susceptible to elicit a T-cell response, compared to state-of-the-art epitope prediction methods. Our results suggest that PrDx may be an attractive prediction tool for neo-epitopes discovery. Citation Format: Stephan Thorgrimsen, Sune Justesen, Nicolas Rapin. Development of prediction software PrDx, trained on peptide-MHC stability assays, shows new important positions in the binding patterns of the peptides-MHC I and II complexes [abstract]. In: Proceedings of the Fourth CRI-CIMT-EATI-AACR International Cancer Immunotherapy Conference: Translating Science into Survival; Sept 30-Oct 3, 2018; New York, NY. Philadelphia (PA): AACR; Cancer Immunol Res 2019;7(2 Suppl):Abstract nr B093.

Computational B-cell epitope identification and production of neutralizing murine antibodies against Atroxlysin-I

Epitope identification is essential for developing effective antibodies that can detect and neutralize bioactive proteins. Computational prediction is a valuable and time-saving alternative for experimental identification. Current computational methods for epitope prediction are underused and undervalued due to their high false positive rate. In this work, we targeted common properties of linear B-cell epitopes identified in an individual protein class (metalloendopeptidases) and introduced an alternative method to reduce the false positive rate and increase accuracy, proposing to restrict predictive models to a single specific protein class. For this purpose, curated epitope sequences from metalloendopeptidases were transformed into frame-shifted Kmers (3 to 15 amino acid residues long). These Kmers were decomposed into a matrix of biochemical attributes and used to train a decision tree classifier. The resulting prediction model showed a lower false positive rate and greater area under the curve when compared to state-of-the-art methods. Our predictions were used for synthesizing peptides mimicking the predicted epitopes for immunization of mice. A predicted linear epitope that was previously undetected by an experimental immunoassay was able to induce neutralizing-antibody production in mice. Therefore, we present an improved prediction alternative and show that computationally identified epitopes can go undetected during experimental mapping.

In silico identification and characterization of putative kuruma prawn (Marsupenaeus japonicus) allergens

Background: Prawn is one of the major sources of shellfish allergens, which will induce mild to severe allergic reactions. Aim: The objective of this study was to predict putative allergens and its characteristics present in Marsupenaeus japonicus through an in silico approach. Materials And Methods: In this present study, in silico tools, RaptorX, Bepipred Linear Epitope and Parker Hydrophilicity Prediction method, and MHC2Pred were used to predict the putative and cross-reactive allergens, tertiary and secondary structures, B-cell linear epitopes, and T-cell epitopes, respectively. Results: As a result, eight putative and cross-reactive allergens including tropomyosin fast isoform, arginine kinase, sarcoplasmic calcium binding protein, myosin light chain, chymotrypsin like proteinase, superoxide dismutase, cathepsin B, and trypsin were predicted by analyzing the protein sequences of M. japonicus. Furthermore, five amino acids (Ala, Ser, Asn, Gly, and Lys) play a vital role in immunoglobulin E binding allergenic epitope. At least one of them was found in the predicted B-cell epitope for each of the predicted putative allergens. Moreover, the predicted T-cell epitopes were highly associated with human leukocyte antigen (HLA) DRB1*0101 and HLA-DRB1*0901 alleles while mediating T-cell immune responses. Conclusion: These results can be utilized to contribute in peptide immunotherapy and reduce the allergic diseases related to shellfish.

Fundamentals and Methods for T- and B-Cell Epitope Prediction.

Adaptive immunity is mediated by T- and B-cells, which are immune cells capable of developing pathogen-specific memory that confers immunological protection. Memory and effector functions of B- and T-cells are predicated on the recognition through specialized receptors of specific targets (antigens) in pathogens. More specifically, B- and T-cells recognize portions within their cognate antigens known as epitopes. There is great interest in identifying epitopes in antigens for a number of practical reasons, including understanding disease etiology, immune monitoring, developing diagnosis assays, and designing epitope-based vaccines. Epitope identification is costly and time-consuming as it requires experimental screening of large arrays of potential epitope candidates. Fortunately, researchers have developed in silico prediction methods that dramatically reduce the burden associated with epitope mapping by decreasing the list of potential epitope candidates for experimental testing. Here, we analyze aspects of antigen recognition by T- and B-cells that are relevant for epitope prediction. Subsequently, we provide a systematic and inclusive review of the most relevant B- and T-cell epitope prediction methods and tools, paying particular attention to their foundations.

T-Cell Epitope Prediction.

An epitope is a part of an immunogenic protein that can be recognized by the immune system. The peptides that can be recognized by the T-cell receptors after a particular antigen has been intracellularly processed, bound to at least one MHC molecule and expressed on the surface of the antigen presenting cell as a MHC-peptide complex, are called a T-cell epitope. Individuals who have at least one MHC molecule able to most avidly bind to allergenic amino acid sequences from an allergen, and at the same time have the appropriate T-cell clone that can recognize this MHC-peptide complex, are expected to be genetically prone to allergic reactions against that allergen. This possibility can be examined in silico by utilizing modern computational techniques that are based on sophisticated mathematics and statistics. The design principles of these techniques are different and therefore variations in their predictions are expected. The available software programs that have been developed on this basis are able to combine the increasing amount and complexity of the available experimental data that have been organized in immunoinformatics databases to predict potential allergen T-cell epitopes. All relevant T-cell epitope prediction methods can be accessed online as a freeware.

Abstract LB-287: Combining immunomics and genomics for immunotherapy of refractory and rare cancers

Abstract Introduction: An underlying basis of cancer immunotherapy is the activation of the patient's own immune effector cells to specifically eradicate tumor cells. Different strategies of cancer immunotherapy have shown outstanding success in a number of patients across various tumor types. However, currently only a fraction of patients respond to immunotherapy, and therapy works only in a subset of tumors. To understand why, an integrated analysis of host and tumor factors is required. Fundamental issues in the analysis of key prognostic and predictive components include elucidating the complex interaction between tumors and their microenvironment, determining the type and densities of tumor-infiltrating immune cells, and understanding how the mutational load influences therapy outcome. Sophisticated analysis is required for patients with refractory or rare cancers to explore new therapeutic strategies. We established a systems medicine approach to obtain detailed data about tumor-host interactions, which may provide information on clinical outcome and help identify patients who are most likely to benefit from immunotherapy. Methods and Results: Whole-exome sequencing of tumor and control samples is performed from freshly obtained patient samples. In addition, whole-transcriptome sequencing of tumor tissue is performed if possible. Single nucleotide variations (SNVs) are detected in each tumor. Sequence-based HLA typing and computational epitope prediction methods are used to assess the immunogenicity of each SNV. The grade of immune cell infiltration and the expression of selected immunomodulatory proteins, e.g. PD-1 and PD-L1, is assessed by immunohistochemical stainings of tissue samples. Additionally, cytokine and chemokine profiles are determined in the tumor microenvironment. The resulting data can aid in treatment decision, e.g. PD-1 blocking antibodies can be administered when elevated PD-1 expression is detected. Alternatively, peptide vaccination is considered if a sufficient number of immunogenic mutations is detected. Based on the mutational profiles, mutation specific 29mer peptides and corresponding wild type peptides are produced for ELIspot assays with patient PBMCs to assess mutation-specific T cell reactivity. To assess the changes over time, blood and tissue are obtained during therapy to monitor therapy-induced changes in the periphery and the tumor microenvironment. Conclusion: Here we present our established workflow to systematically analyze tumor-host interactions in cancer patients and show preliminary data of different facets and clinical implications of these complex interactions. To evaluate and validate immunological and pathological characteristics of a tumor and the methods to assess its immunogenicity as well as its complex interplay with the microenvironment, comprehensive translational studies have to be conducted. Eventually, the obtained information will help to identify important subgroups and patients who are most likely to benefit from a certain type of immunotherapy. Citation Format: Zeynep Kosaloglu, Inka Zoernig, Niels Halama, Bruno Koehler, Anne Katrin Berger, Christoph Springfeld, Alexis Ulrich, Martin Schneider, Felix Lasitschka, Hanno Glimm, Stefan Froehling, Benedikt Brors, Dirk Jaeger. Combining immunomics and genomics for immunotherapy of refractory and rare cancers. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-287.

HLaffy: estimating peptide affinities for Class-1 HLA molecules by learning position-specific pair potentials.

T-cell epitopes serve as molecular keys to initiate adaptive immune responses. Identification of T-cell epitopes is also a key step in rational vaccine design. Most available methods are driven by informatics and are critically dependent on experimentally obtained training data. Analysis of a training set from Immune Epitope Database (IEDB) for several alleles indicates that the sampling of the peptide space is extremely sparse covering a tiny fraction of the possible nonamer space, and also heavily skewed, thus restricting the range of epitope prediction. We present a new epitope prediction method that has four distinct computational modules: (i) structural modelling, estimating statistical pair-potentials and constraint derivation, (ii) implicit modelling and interaction profiling, (iii) feature representation and binding affinity prediction and (iv) use of graphical models to extract peptide sequence signatures to predict epitopes for HLA class I alleles. HLaffy is a novel and efficient epitope prediction method that predicts epitopes for any Class-1 HLA allele, by estimating the binding strengths of peptide-HLA complexes which is achieved through learning pair-potentials important for peptide binding. It relies on the strength of the mechanistic understanding of peptide-HLA recognition and provides an estimate of the total ligand space for each allele. The performance of HLaffy is seen to be superior to the currently available methods. The method is made accessible through a webserver http://proline.biochem.iisc.ernet.in/HLaffy : nchandra@biochem.iisc.ernet.in Supplementary data are available at Bioinformatics online.