Epitope Prediction Algorithms Research Articles

DiscovEpi: automated whole proteome MHC-I-epitope prediction and visualization

BackgroundAntigen presentation is a central step in initiating and shaping the adaptive immune response. To activate CD8+ T cells, pathogen-derived peptides are presented on the cell surface of antigen-presenting cells bound to major histocompatibility complex (MHC) class I molecules. CD8+ T cells that recognize these complexes with their T cell receptor are activated and ideally eliminate infected cells. Prediction of putative peptides binding to MHC class I (MHC-I) is crucial for understanding pathogen recognition in specific immune responses and for supporting drug and vaccine design. There are reliable databases for epitope prediction algorithms available however they primarily focus on the prediction of epitopes in single immunogenic proteins.ResultsWe have developed the tool DiscovEpi to establish an interface between whole proteomes and epitope prediction. The tool allows the automated identification of all potential MHC-I-binding peptides within a proteome and calculates the epitope density and average binding score for every protein, a protein-centric approach. DiscovEpi provides a convenient interface between automated multiple sequence extraction by organism and cell compartment from the database UniProt for subsequent epitope prediction via NetMHCpan. Furthermore, it allows ranking of proteins by their predicted immunogenicity on the one hand and comparison of different proteomes on the other. By applying the tool, we predict a higher immunogenic potential of membrane-associated proteins of SARS-CoV-2 compared to those of influenza A based on the presented metrics epitope density and binding score. This could be confirmed visually by comparing the epitope maps of the influenza A strain and SARS-CoV-2.ConclusionAutomated prediction of whole proteomes and the subsequent visualization of the location of putative epitopes on sequence-level facilitate the search for putative immunogenic proteins or protein regions and support the study of adaptive immune responses and vaccine design.

Reverse vaccinology approach for identification of epitopes from E1 protein as peptide vaccine against HCV: A proof of concept

The development of effective vaccines against Hepatitis C Virus (HCV) remains a global health priority and challenge. In this study, we employed an integrative approach combining computational epitope prediction with experimental validation to identify immunogenic peptides targeting the E1 glycoprotein of HCV. In the present report, computational data from various epitope prediction algorithms such as IEDB and SYFPEITHI, followed by molecular dynamics (MD) simulations and immuno-informatics analysis is presented. Through computational screening, we identified potential epitope candidates, with QVRNSSGLY (P3) and QLFTFSPRH (P7) emerging as promising candidates. MD simulations revealed stable interactions between these epitopes and MHC molecule, further validated by free energy estimations using MMPBSA method. Immuno-informatics analysis supported these findings, showing high binding potential and immunogenicity scores for the selected peptides. Subsequent synthesis and characterization of epitope peptides confirmed their structural integrity and purity required for conducting immune activation assays. Experimental immunological assays carried out in this study involved epitope peptide induced activation of CD8 + and CD4 + T cells from healthy human subjects and HCV- recovered patients. Data from experimental validation revealed significant cytokine release upon exposure to epitope peptides, particularly TNF-a, IL-6, and GM-CSF, indicative of robust immune responses. Notably, peptides P3 and P7 exhibited the most pronounced cytokine induction profiles, underscoring their potential as vaccine candidates. Further investigations addressing the mechanism of action of these epitope peptides under preclinical and clinical settings may help in developing effective vaccine against HCV.

Designing a Conserved Immunogenic Peptide Construct from the Nucleocapsid Protein of Puumala orthohantavirus.

Puumala orthohantavirus (PUUV) is an emerging zoonotic virus endemic to Europe and Russia that causes nephropathia epidemica, a mild form of hemorrhagic fever with renal syndrome (HFRS). There are limited options for treatment and diagnosis of orthohantavirus infection, making the search for potential immunogenic candidates crucial. In the present work, various bioinformatics tools were employed to design conserved immunogenic peptides containing multiple epitopes of PUUV nucleocapsid protein. Eleven conserved peptides (90% conservancy) of the PUUV nucleocapsid protein were identified. Three conserved peptides containing multiple T and B cell epitopes were selected using a consensus epitope prediction algorithm. Molecular docking using the HPEP dock server demonstrated strong binding interactions between the epitopes and HLA molecules (ten alleles for each class I and II HLA). Moreover, an analysis of population coverage using the IEDB database revealed that the identified peptides have over 90% average population coverage across six continents. Molecular docking and simulation analysis reveal a stable interaction with peptide constructs of chosen immunogenic peptides and Toll-like receptor-4. These computational analyses demonstrate selected peptides' immunogenic potential, which needs to be validated in different experimental systems.

TRLS-05. A PILOT STUDY TO ASSESS THE SAFETY, FEASIBILITY, AND PRELIMINARY EFFICACY OF A NEOEPITOPE-BASED PERSONALIZED DNA VACCINE APPROACH IN PEDIATRIC PATIENTS WITH RECURRENT BRAIN TUMORS

Abstract BACKGROUND Pediatric brain tumors present a heterogeneous group of neoplasms with limited treatment options, especially at relapse. Hence, innovative therapeutic strategies are imperative. Immunotherapy, specifically immunogenomics, utilizing personalized vaccines based on tumor-specific neoantigens, has emerged as a promising option in other tumors. However, the feasibility of this approach in pediatric brain tumors remain largely unexplored. METHODS This single institution feasibility study focuses on sequencing analyses of pediatric brain tumors to identify mutant tumor-specific antigens. The study employs tumor/normal exome sequencing and cDNA-capture sequencing to identify and confirm mutant antigens, respectively. An epitope prediction algorithm prioritizes these antigens with subsequent confirmation of binding to HLA class I molecules. The neoantigen DNA vaccine strategy, utilizing a polyepitope DNA vaccine, is designed to target up to 20 prioritized antigens. The TDS-IM v2.0 device facilitates the administration of the vaccine. The primary objective of the study is to determine the feasibility of adjuvant personalized neoantigen DNA vaccine administration in patients with recurrent or refractory pediatric brain tumors. RESULTS We anticipate the identification of actionable neoantigens in pediatric brain tumors to support the potential efficacy of personalized DNA vaccines. The proposed sequencing strategies, epitope prediction algorithm, and in vitrobinding studies aim to establish a comprehensive methodology for neoantigen identification and prioritization. During the meeting, we will present the study background, rationale, detailed methodology, and design to inform the brain tumor community of this study. CONCLUSION This feasibility study is poised to provide essential insights into the development of personalized neoantigen DNA vaccines for recurrent or refractory pediatric brain tumors and aims to demonstrate safety, feasibility, and potential efficacy in targeting mutant tumor-specific antigens. The findings from this study will lay the groundwork for future clinical trials, evaluating the impact of personalized immunotherapy on progression-free and overall survival rates in this challenging patient population.

Combining the potency of a neoantigen-expressing DNA vector, an anti-CTLA-4 antibody, and an attenuated poxvirus in a personalized immunization platform: Preclinical studies of ODI-2001.

e14674 Background: Immune checkpoint inhibitors (ICI) have transformed cancer treatment. Their impressive effectiveness is crucially dependent on pre-existing neoepitope-specific immune responses. ODI-2001 aims at increasing the immune response against tumors by combining a DNA expressing multiple neoepitopes of the tumor, a MVA vaccinia virus as physiologic adjuvant and DNA carrier, and an anti-CTLA4 antibody as amplifier of the immune response. Methods: ODI-2001 was administered by repeated subcutaneous injections after tumor cells implantation in mice melanoma model B16F10 and colorectal cancer model CT26, and both before and after implantation in breast cancer model 4T1. In B16F10 model combination of ODI-2001 with anti-PD1 was assessed also. Neoepitopes were chosen from literature in B16F10 and CT26 models. In 4T1 model they were predicted using a dedicated neoepitope prediction algorithm (ImmunoEngine, myNEO, Gent, Belgium). Tumor volume and mice survival were assessed over time. Tumors were also analysed by immunofluorescence for immune cell infiltration. Each experiment received approval from the ethics committee for animals. Results: A significant improvement in survival was observed in mice treated with ODI-2001 in B16F10, and CT26 syngeneic mice models. In B16F10 the combination of ODI-2001 with anti-PD1 antibody demonstrated synergistic activity and a significant improvement in survival, while anti-PD1 alone or combined with anti-CTLA-4 failed to show any therapeutic effect (ODI-2001 + anti-PD1 vs. anti-CTLA-4 + anti-PD1 p<0.01). ODI-2001 treatment was associated with tumor rejections in the three animal models. In 4T1 model the ImmunoEngine epitope prediction algorithm demonstrated an improved efficacy as compared to a set of neoepitopes chosen from the literature. In B16F10, following immunization with ODI-2001, an increased CD8 immune cell infiltration of the tumor was documented. ODI-2001 was well tolerated by the treated animals. Conclusions: These data demonstrate a significant antitumoral activity of ODI-2001 as a neoantigen immunization platform across different preclinical cancer models, both in monotherapy and combined with anti-PD1. ODI-2001 represents a promising option to induce de novo cancer-specific immune responses, potentially increasing the proportion of patients responding to immunotherapy treatments. Based on this preclinical program a phase I with ODI-2001 in patients with advanced colorectal carcinoma is currently under preparation.

Abstract SY41-01: Selection of targeted neoantigens for personalized cancer vaccines

Abstract Neoantigen targeting personalized cancer vaccines (PCV) have the potential to revolutionize cancer treatment. These personalized vaccines rely on accurate epitope prediction and selection. We previously reported phase I trials of neoantigen targeting PCV in melanoma and glioblastoma multiforme (GBM). Recently we concluded a trial in renal cell carcinoma (RCC) where the neoantigen targeting PCV was immunogenic in all patients, targeted cancer drivers, and demonstrated in vitro reactivity to primary patient tumors. The neoantigen selection pipeline at DFCI supporting clinical trials utilizes immunopeptidome-trained epitope prediction algorithms to design synthetic long peptides as vaccine antigens containing predicted HLA Class I neoantigens. We also prioritize highly expressed neoantigens and neoantigens originating from cancer drivers as vaccine epitopes. For future iterations of this pipeline, we aspire to include novel classes of neoantigens originating from endogenous human retroviruses (ERV), fusion epitopes, novel or unannotated open reading frames (nuORFs), and immunopeptidome-identified neoantigens. The neoantigen selection pipeline is currently supporting multiple cancer vaccine trials at DFCI targeting melanoma, GBM, RCC, ovarian cancer, and chronic lymphocytic leukemia (CLL). Citation Format: Derin Keskin. Selection of targeted neoantigens for personalized cancer vaccines [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY41-01.

Abstract 4111: Multi-peptide cancer vaccines targeting KRAS induce significant anti-tumor efficacy

Abstract Background and objective: Kirsten rat sarcoma viral oncogene homologue (KRAS) is frequently mutated in human cancers. KRAS mutants offer the potential for the development of cancer-specific immunotherapy, as epitopes harbouring mutations are strong neoantigens to which the immune system generates cancer-specific anti-tumor effects. However, previous clinical trials of KRAS-targeted vaccines failed to generate satisfactory anti-cancer efficacy. Evidence suggested that CD4 T cell activity plays a critical role in augmenting the cytotoxic effect of CD8 T cells. Here, we design long peptides based on the MHC II hotspots on the entire KRAS protein that activate both CD4 and CD8 T cells to kill KRAS-driven cancer cells. Long peptides also cover a wider range of HLA alleles to increase their utility in a broader spectrum of patients. Our objective is to create a multi-peptide vaccine simultaneously targeting the mutant and wild-type (WT) regions of human KRAS protein. Methods: By selecting MHC II hotspots via multiple epitope prediction algorithms, we designed and synthesized 8 long peptides with lengths between 25 to 30mers. One of them harboured a G12D mutation, while the others covered the WT regions of KRAS. In addition, the corresponding keyhole limpet hemocyanin (KLH) conjugated peptides were also generated. We first demonstrated their immunogenicity in mouse in vivo models. Different combinations of peptides adjuvanted with either CpG oligodeoxynucleotide + aluminum (CpG+alum) or complete/incomplete Freud’s adjuvant (CFA/IFA) were immunized into Balb/c mice before determining the immune response by mouse interferon γ (mIFNγ) ELISPOT assay. Subsequently, an in vivo prophylactic anti-tumor study was conducted by immunizing this multi-peptide vaccine in Balb/c mice and observing the growth inhibition of the CT26-derived tumors. Results: T cell responses were observed in two peptide mixes with or without G12D mutation, suggesting that both mutant-harbouring peptide and WT peptides were immunogenic. We showed that KLH-conjugated peptides and CpG+alum elicited stronger immune responses than the naked peptides and CFA/IFA respectively. Furthermore, peptide mapping showed that the G12D mutant-harbouring peptide and two other WT peptides may contribute to the overall T cell response in the Balb/c mouse. In the prophylactic anti-tumor study (n = 10), immunization of KRAS naked peptide and KLH-conjugated peptide vaccines significantly inhibited tumor growth by 51.2% (****p < 0.0001) and 44.7% (***p < 0.001) respectively, compared to control. Subsequent mIFNγ ELISPOT assay showed that the tumor inhibitory effect was attributed to the activation of antigen-specific T cell response of the vaccines. Conclusion: Our multi-peptide vaccines targeting both the mutant and WT regions of KRAS could generate KRAS-specific T cell responses and elicit significant anti-tumor effect in mouse models. Citation Format: Chi Han Samson Li, Melvin Toh. Multi-peptide cancer vaccines targeting KRAS induce significant anti-tumor efficacy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4111.

An In silico Study on B-cell Epitope Mapping of Acinetobacter baumannii Outer Membrane Protein K.

Acinetobacter baumannii is one of the main causes of nosocomial infections. No vaccine has yet been licensed for use in humans, and efforts are still ongoing. In the present study, we have predicted the B-cell epitopes of A. baumannii's outer membrane protein K (OMPK) by using epitope prediction algorithms as possible vaccine candidates for future studies. The linear B-cell epitopes were predicted by seven different prediction tools. The 3D structure of OMPK was modeled and used for discontinuous epitope prediction by ElliPro and DiscoTope 2.0 tools. The final linear epitopes and the discontinuous epitope segments were checked for potential allergenicity, toxicity, human similarity, and experimental records. The structure and physicochemical features of the final epitopic peptide were assessed by numerous bioinformatics tools. Many B-cell epitopes were detected that could be assessed for possible antigenicity and immunogenicity. Also, an epitopic 22-mer region (peptide) of OMPK was found that contained both linear and discontinuous B-cell epitopes. This epitopic peptide has been found to possess appropriate physicochemical and structural properties to be an A. baumannii vaccine candidate. Altogether, here, the high immunogenic B-cell epitopes of OMPK have been identified, and a high immunogenic 22-mer peptide as an A. baumannii vaccine candidate has been introduced. The in vitro/in vivo studies of this peptide are recommended to decide its real efficacy and efficiency.

Allele Frequency Net Database.

The allele frequency net database (AFND, http://www.allelefrequencies.net ) is an online web-based repository that contains information on the frequencies of immune-related genes and their corresponding alleles in worldwide human populations. At present, the website contains data from 1784 population samples in more than 14 million individuals from 129 countries on the frequency of genes from different polymorphic regions including data for the human leukocyte antigen (HLA) system. In addition, over the last four years, AFND has also incorporated genotype raw data from 85,000 individuals comprising 215 population samples from 39 countries. Moreover, more population data sets containing next generation sequencing data spanning >3 million individuals have been added. This resource has been widely used in a variety of contexts such as histocompatibility, immunology, epidemiology, pharmacogenetics, epitope prediction algorithms for population coverage in vaccine development, population genetics, among many others. In this chapter, we present an update of the most used searching mechanisms as described in a previous volume and some of the latest developments included in AFND.

A multifaceted approach for identification, validation, and immunogenicity of naturally processed and in silico-predicted highly conserved SARS-CoV-2 peptides

SARS-CoV-2 remains a major global public health concern. Antibody waning and immune escape variant emergence necessitate the development of next generation vaccines that induce cross-reactive durable immune responses. T cell responses to SARS-CoV-2 demonstrate higher conservation, antigenic breadth, and longevity than antibody responses. Therefore, we sought to identify pathogen-derived T cell epitopes for a potential peptide-based vaccine. We pursued an approach leveraging: 1) liquid chromatography and tandem mass spectrometry (LC-MS/MS)-based identification of peptides from ancestral SARS-CoV-2-infected cell lines, 2) epitope prediction algorithms, and 3) overlapping peptide libraries. From this strategy, we identified 380 unique SARS-CoV-2-derived peptide sequences, including 53 antigenic HLA class I and class II peptides from multiple structural and non-structural/accessory viral proteins. These peptide sequences were highly conserved across variants of concern/interest (VoC/VoIs), and are estimated to achieve coverage of >96% of the world population. Our findings validate this discovery pipeline for peptide identification and immunogenicity testing, and are a crucial step toward the development of a next-generation multi-epitope SARS-CoV-2 peptide vaccine, and a novel vaccine platform methodology.

In silico epitope prediction and evolutionary analysis reveals capsid mutation patterns for enterovirus B.

Enterovirus B (EVB) is a common species of enterovirus, mainly consisting of Echovirus (Echo) and Coxsackievirus B (CVB). The population is generally susceptible to EVB, especially among children. Since the 21st century, EVB has been widely prevalent worldwide, and can cause serious diseases, such as viral meningitis, myocarditis, and neonatal sepsis. By using cryo-electron microscopy, the three-dimensional (3D) structures of EVB and their uncoating receptors (FcRn and CAR) have been determined, laying the foundation for the study of viral pathogenesis and therapeutic antibodies. A limited number of epitopes bound to neutralizing antibodies have also been determined. It is unclear whether additional epitopes are present or whether epitope mutations play a key role in molecular evolutionary history and epidemics, as in influenza and SARS-CoV-2. In the current study, the conformational epitopes of six representative EVB serotypes (E6, E11, E30, CVB1, CVB3 and CVB5) were systematically predicted by bioinformatics-based epitope prediction algorithm. We found that their epitopes were distributed into three clusters, where the VP1 BC loop, C-terminus and VP2 EF loop were the main regions of EVB epitopes. Among them, the VP1 BC loop and VP2 EF loop may be the key epitope regions that determined the use of the uncoating receptors. Further molecular evolution analysis based on the VP1 and genome sequences showed that the VP1 C-terminus and VP2 EF loop, as well as a potential "breathing epitope" VP1 N-terminus, were common mutation hotspot regions, suggesting that the emergence of evolutionary clades was driven by epitope mutations. Finally, footprints showed mutations were located on or near epitopes, while mutations on the receptor binding sites were rare. This suggested that EVB promotes viral epidemics by breaking the immune barrier through epitope mutations, but the mutations avoided the receptor binding sites. The bioinformatics study of EVB epitopes may provide important information for the monitoring and early warning of EVB epidemics and developing therapeutic antibodies.

Dynamic structural analysis-based epitope prediction of Exendin-4 in aqueous solution.

The study of epitopes has a broad range of applications in drug discovery, vaccine design, and immunotherapy. In this study, an epitope prediction method was developed based on the dynamic structure of protein antigens. Solvent accessible surface area, charge, and root mean square fluctuation were introduced as the key residue property parameters. The epitope prediction algorithm was established by constructing a three-parameter complex metrics of seven-peptide groups. The method was applied to predict the epitopes of Exendin-4, an effective antidiabetic drug. The epitopes of both the natural and C-terminal amidated forms of Exendin-4 were predicted and compared in their folded and intermediate states. In the folded state, the epitopes of natural Exendin-4 (His1-Phe6 and Asp9-Val19) were found to be nearly identical to the epitopes of C-terminal aminated Exendin-4 (His1-Thr7 and Asp9-Val19). In the intermediate state, however, the epitopes of natural Exendin-4 (His1-Gly4, Phe6 and Lys12-Arg20) covered fewer amino acids than the epitopes of C-terminal aminated Exendin-4 (His1-Gly4, Phe6, Asp9-Val19 and Trp25-Lys27). The comparison with the results from other prediction tools demonstrates the reliability of our predicted epitopes of Exendin-4.

Immunoinformatics-Based Identification of the Conserved Immunogenic Peptides Targeting of Zika Virus Precursor Membrane Protein.

Zika virus infections lead to neurological complications such as congenital Zika syndrome and Guillain-Barré syndrome. Rising Zika infections in newborns and adults have triggered the need for vaccine development. In the current study, the precursor membrane (prM) protein of the Zika virus is explored for its functional importance and design of epitopes enriched conserved peptides with the usage of different immunoinformatics approach. Phylogenetic and mutational analyses inferred that the prM protein is highly conserved. Three conserved peptides containing multiple T and B cell epitopes were designed by employing different epitope prediction algorithms. IEDB population coverage analysis of selected peptides in six different continents has shown the population coverage of 60-99.8% (class I HLA) and 80-100% (class II HLA). Molecular docking of selected peptides/epitopes was carried out with each of class I and II HLA alleles using HADDOCK. A majority of peptide-HLA complex (pHLA) have HADDOCK scores found to be comparable and more than native-HLA complex representing the good binding interaction of peptides to HLA. Molecular dynamics simulation with best docked pHLA complexes revealed that pHLA complexes are stable with RMSD <5.5Å. Current work highlights the importance of prM as a strong antigenic protein and selected peptides have the potential to elicit humoral and cell-mediated immune responses.

Identification of antigenic epitopes recognized by tumor infiltrating lymphocytes in high grade serous ovarian cancer by multi-omics profiling of the auto-antigen repertoire

Immunotherapeutic strategies aimed at enhancing tumor cell killing by tumor-specific T cells hold great potential for reducing tumor burden and prolonging survival of cancer patients. Although many potential tumor antigens have been described, identifying relevant targets when designing anti-cancer vaccines or targeted cell therapies remains a challenge. To identify novel, potentially immunogenic candidate tumor antigens, we performed integrated tumor transcriptomic, seromic, and proteomic analyses of high grade serous ovarian cancer (HGSC) patient tumor samples. We identified tumor neo-antigens and over-expressed antigens using whole exome and RNA sequencing and examined these in relation to patient-matched auto-antibody repertoires. Focusing on MHC class I epitopes recognized by CD8+ T cells, HLA-binding epitopes were identified or predicted from the highly expressed, mutated, or auto-antibody target antigen, or MHC-associated peptides (MAPs). Recognition of candidate antigenic peptides was assessed within the tumor-infiltrating T lymphocyte (TIL) population expanded from each patient. Known tumor-associated antigens (TAA) and cancer/testis antigens (CTA) were commonly found in the auto-antibody and MAP repertoires and CD8+ TILs recognizing epitopes from these antigens were detected, although neither expression level nor the presence of auto-antibodies correlated with TIL recognition. Auto-antibodies against tumor-mutated antigens were found in most patients, however, no TIL recognition of the highest predicted affinity neo-epitopes was detected. Using high expression level, auto-antibody recognition, and epitope prediction algorithms, we identified epitopes in 5 novel antigens (MOB1A, SOCS3, TUBB, PRKAR1A, CCDC6) recognized by HGSC patient TILs. Furthermore, selection of epitopes from the MAP repertoire identified 5 additional targets commonly recognized by multiple patient TILs. We find that the repertoire of TIL specificities includes recognition of highly expressed and immunogenic self-antigens that are processed and presented by tumors. These results indicate an ongoing autoimmune response against a range of self-antigens targeted by HGSC TILs.

Personalized dendritic cell vaccination in cancer therapy: An evidence-based research study.

Although chemotherapy is considered to be the golden standard, it does not come without a price. Toxicities and resistance are frequently limiting its effectiveness. Immunotherapy has emerged as a safer therapeutic alternative but still has a long way until it has proven to be of equal efficacy. A type of immunotherapy is dendritic cell (DC) vaccination. We have developed a novel platform for the generation of autologous DCs that have been activated against peptides that are personalized for each patient individually. The aim of the study was to clinically evaluate this platform. Our platform and our algorithm for the determination of the immunogenic peptides has been tested. DC generation was verified both morphologically and by CD80/86 expression. Peptide antigenicity was determined using a number of T-cell epitope prediction algorithms. Response to therapy was evaluated using response evaluation criteria in solid tumors (RECIST) criteria by the doctors involved. Immune status was also evaluated before and after DC vaccination and correlated with circulated tumor cell count. It was found that DC vaccine increased immune activation while correlated with decreased circulating tumor cell counts. Clinical evaluation by the determination of immune markers may be a superior tool than using RECIST criteria. Dendritic cell therapies could prove to be a valuable tool in cancer treatment.

Neoantigen vaccine and neoantigen-specific cell adoptive transfer therapy in solid tumors: Challenges and future directions.

The phenomenon of tumor hierarchy and genetic instability can be explained by the "two-hits theory" and results in the occurrence of many somatic mutations. The expression of nonsynonymous mutations results in the production of mutant proteins from tumor cells, namely tumor-specific antigens called neoantigens. Because neoantigens do not exist in healthy cells, they have the potential to stimulate antitumor immune responses by CD4+ and CD8+ T-cell activation without jeopardizing normal tissues. Immunotherapy has reshaped the cancer treatment paradigm in recent decades with the introduction of immune-checkpoint blockade therapy and transgenic T-cell receptor/chimeric antigen receptor T cells. However, these strategies performed poorly in solid tumors because of the obstacles of the immunosuppressive microenvironment caused by regulatory T cells and other suppressor cells. Therefore, other immunotherapeutic strategies are under development, such as personalized vaccines, to trigger de novo T-cell responses against neoantigens and lead to the amplification of tumor-specific T-cell subclones. Neoantigen epitope prediction algorithms have enabled the detection of neoantigens and the creation of tailored neoantigen vaccines as a result of the fast development of next-generation sequencing and cancer bioinformatics. Here we provide an overview of the current neoantigen cancer vaccines and adoptive T-cell transfer therapy with neoantigen-specific lymphocytes. We also discuss the challenges in developing neoantigen-targeted immunotherapeutic strategies for cancer.

Preinduced reovirus-specific T-cell immunity enhances the anticancer efficacy of reovirus therapy

BackgroundMany solid tumors do not respond to immunotherapy due to their immunologically cold tumor microenvironment (TME). We and others found that oncolytic viruses (OVs), including reovirus type 3 Dearing, can...

BepiTBR: T-B reciprocity enhances B cell epitope prediction

SummaryThe ability to predict B cell epitopes is critical for biomedical research and many clinical applications. Investigators have observed the phenomenon of T-B reciprocity, in which candidate B cell epitopes with nearby CD4+ T cell epitopes have higher chances of being immunogenic. To our knowledge, existing B cell epitope prediction algorithms have not considered this interesting observation. We developed a linear B cell epitope prediction model, BepiTBR, based on T-B reciprocity. We showed that explicitly including the enrichment of putative CD4+ T cell epitopes (predicted HLA class II epitopes) in the model leads to significant enhancement in the prediction of linear B cell epitopes. Curiously, the positive impact on B cell epitope generation is specific to the enrichment of DQ allele binders. Overall, our work provides interesting mechanistic insights into the generation of B cell epitopes and points to a new avenue to improve B cell epitope prediction for the field.

Factor VIII Epitope Analysis Using a Random Peptide Phage-Display Library Approach in the Sippet Cohort

Background Inhibitor development is the most severe complication of hemophilia A care, and is associated with increased morbidity and mortality.Aims The aim of this study was to use a novel epitope mapping method to explore the factor VIII (FVIII)-specific epitope profile in the SIPPET cohort population.Methods The population consisted of 122 previously untreated patients with severe hemophilia A that were followed-up for 50 days of exposure to FVIII. Sampling was performed before FVIII treatment and at the end of the follow-up. The outcome was inhibitor development. The FVIII-specific IgG epitope repertoire was assessed by means of a novel high-throughput epitope mapping technique using a random peptide phage-display library. Using this assay, a set of affinity-selected 12-mer peptide sequences (also called mimotopes) that were strongly bound by FVIII-specific antibodies were identified. These mimotopes were clustered on the basis of sequence similarity and a consensus motif was generated for each mimotope cluster. Discriminative performance of these mimotope clusters was assessed by ROC analysis. Mimotope clusters were mapped onto the 3D structure of a B-domain deleted FVIII model using a B-cell epitope prediction algorithm (Mapitope).Results The FVIII-specific antibody response is polyclonal with several mimotope clusters. The most predominant mimotope clusters in inhibitor patients were mapped to the heavy chain of the FVIII molecule. Using plasma samples taken before exposure to FVIII, three mimotopes (with the consensus motifs “QM”, “PSLxWK” and “SWPHxxxxK”) were identified that predicted inhibitor development (with an AUC of 0.76, 0.80 and 0.76 respectively).Conclusion Information on immunodominant epitope clusters can be used to generate novel, less immunogenic FVIII proteins and set up diagnostic tests that predict the risk of inhibitor development before starting treatment with FVIII. [Display omitted] DisclosuresPalm: Protobios LLC: Current Employment, Patents & Royalties: Inventor of the patent application (PCT Application No. US/14079626) filed by Protobios that covers the use of phage display method to manipulate and monitor humoral immunity.. Palla: Pfizer: Other: Travel support; Kedrion: Other: Travel support; Novonordisk: Speakers Bureau. Peyvandi: Roche: Consultancy, Honoraria; Sanofi: Consultancy, Honoraria; Sobi: Consultancy, Honoraria; Takeda: Consultancy, Honoraria.

Antigenic Structural Similarity as a Predictor for Antibody Cross-Reactivity

Antibodies are an essential component of the adaptive immune system that function to neutralize foreign invaders, such as bacterial and parasitic pathogens. However, B-cell epitopes remain difficult to predict due to their general indistinguishability from other protein regions. Epitope prediction tools in the past have largely relied on amino acid sequence similarity; however, implementing three-dimensional protein structure analyses into the epitope prediction algorithms has been shown to increase detection accuracy. Furthermore, structural comparisons between antigenic proteins for their potential to bind cross-reactive antibodies have not been explored extensively in the literature. Recent studies have pointed to the utility of looking at shared epitope structures in predicting antibody crossreactivity, which may shed light on cross-immunity between infectious pathogens and autoimmune diseases induced after infection. Thus, herein, the potential impact of including structural similarity comparisons in detecting shared epitopes is discussed. With the large amount of structural information being determined by three-dimensional computational protein modelling methods, the ability to perform these analyses is becoming more feasible.