Th1 epitope selection for clinically effective cancer vaccines.
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ABSTRACT:
SUBMITTER: Disis ML
PROVIDER: S-EPMC4292222 | biostudies-other | 2014 Oct
REPOSITORIES: biostudies-other
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Project description:MotivationConceptually, epitope-based vaccine design poses two distinct problems: (i) selecting the best epitopes to elicit the strongest possible immune response and (ii) arranging and linking them through short spacer sequences to string-of-beads vaccines, so that their recovery likelihood during antigen processing is maximized. Current state-of-the-art approaches solve this design problem sequentially. Consequently, such approaches are unable to capture the inter-dependencies between the two design steps, usually emphasizing theoretical immunogenicity over correct vaccine processing, thus resulting in vaccines with less effective immunogenicity in vivo.ResultsIn this work, we present a computational approach based on linear programming, called JessEV, that solves both design steps simultaneously, allowing to weigh the selection of a set of epitopes that have great immunogenic potential against their assembly into a string-of-beads construct that provides a high chance of recovery. We conducted Monte Carlo cleavage simulations to show that a fixed set of epitopes often cannot be assembled adequately, whereas selecting epitopes to accommodate proper cleavage requirements substantially improves their recovery probability and thus the effective immunogenicity, pathogen and population coverage of the resulting vaccines by at least 2-fold.Availability and implementationThe software and the data analyzed are available at https://github.com/SchubertLab/JessEV.Supplementary informationSupplementary data are available at Bioinformatics online.
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Project description:Unlike traditional cancer therapies, cancer vaccines (CVs) harness a high specificity of the host's immunity to kill tumor cells. CVs can train and bolster the patient's immune system to recognize and eliminate malignant cells by enhancing immune cells' identification of antigens expressed on cancer cells. Various features of antigens like immunogenicity and avidity influence the efficacy of CVs. Therefore, the choice and application of antigens play a critical role in establishing and developing CVs. Tumor-associated antigens (TAAs), a group of proteins expressed at elevated levels in tumor cells but lower levels in healthy normal cells, have been well-studied and developed in CVs. However, immunological tolerance, HLA restriction, and adverse events are major obstacles that threaten TAA-based CVs' efficacy due to the "self-protein" characteristic of TAAs. As "abnormal proteins" that are completely absent from normal cells, tumor-specific antigens (TSAs) can trigger a robust immune response against tumor cells with high specificity and without going through central tolerance, contributing to cancer vaccine development feasibility. In this review, we focus on the unique features of TAAs and TSAs and their application in vaccines, summarizing their performance in preclinical and clinical trials.
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Project description:BackgroundOverexpression of nonmutated proteins involved in oncogenesis is a mechanism by which such proteins become immunogenic. We questioned whether overexpressed colorectal cancer associated proteins found at higher incidence and associated with poor prognosis could be effective vaccine antigens. We explored whether vaccines targeting these proteins could inhibit the development of intestinal tumors in the azoxymethane (AOM)-induced colon model and APC Min mice.MethodsHumoral immunity was evaluated by ELISA. Web-based algorithms identified putative Class II binding epitopes of the antigens. Peptide and protein specific T-cells were identified from human peripheral blood mononuclear cells using IFN-gamma ELISPOT. Peptides highly homologous between mouse and man were formulated into vaccines and tested for immunogenicity in mice and in vivo tumor challenge. Mice treated with AOM and APC Min transgenic mice were vaccinated and monitored for tumors.ResultsSerum IgG for CDC25B, COX2, RCAS1, and FASCIN1 was significantly elevated in colorectal cancer patient sera compared to volunteers (CDC25B p=0.002, COX-2 p=0.001, FASCIN1 and RCAS1 p<0.0001). Epitopes predicted to bind to human class II MHC were identified for each protein and T-cells specific for both the peptides and corresponding recombinant protein were generated from human lymphocytes validating these proteins as human antigens. Some peptides were highly homologous between mouse and humans and after immunization, mice developed both peptide and protein specific IFN-γ-secreting cell responses to CDC25B, COX2 and RCAS1, but not FASCIN1. FVB/nJ mice immunized with CDC25B or COX2 peptides showed significant inhibition of growth of the syngeneic MC38 tumor compared to control (p<0.0001). RCAS1 peptide vaccination showed no anti-tumor effect. In the prophylactic setting, after immunization with CDC25B or COX2 peptides mice treated with AOM developed significantly fewer tumors as compared to controls (p<0.0002) with 50% of mice remaining tumor free in each antigen group. APC Min mice immunized with CDC25B or COX2 peptides developed fewer small bowel tumors as compared to controls (p=0.01 and p=0.02 respectively).ConclusionsImmunization with CDC25B and COX2 epitopes consistently suppressed tumor development in each model evaluated. These data lay the foundation for the development of multi-antigen vaccines for the treatment and prevention of colorectal cancer.
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Project description:Epitope-based vaccines (EVs) have a wide range of applications: from therapeutic to prophylactic approaches, from infectious diseases to cancer. The development of an EV is based on the knowledge of target-specific antigens from which immunogenic peptides, so-called epitopes, are derived. Such epitopes form the key components of the EV. Due to regulatory, economic, and practical concerns the number of epitopes that can be included in an EV is limited. Furthermore, as the major histocompatibility complex (MHC) binding these epitopes is highly polymorphic, every patient possesses a set of MHC class I and class II molecules of differing specificities. A peptide combination effective for one person can thus be completely ineffective for another. This renders the optimal selection of these epitopes an important and interesting optimization problem. In this work we present a mathematical framework based on integer linear programming (ILP) that allows the formulation of various flavors of the vaccine design problem and the efficient identification of optimal sets of epitopes. Out of a user-defined set of predicted or experimentally determined epitopes, the framework selects the set with the maximum likelihood of eliciting a broad and potent immune response. Our ILP approach allows an elegant and flexible formulation of numerous variants of the EV design problem. In order to demonstrate this, we show how common immunological requirements for a good EV (e.g., coverage of epitopes from each antigen, coverage of all MHC alleles in a set, or avoidance of epitopes with high mutation rates) can be translated into constraints or modifications of the objective function within the ILP framework. An implementation of the algorithm outperforms a simple greedy strategy as well as a previously suggested evolutionary algorithm and has runtimes on the order of seconds for typical problem sizes.
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Project description:A large body of data both in animals and humans demonstrates that the gut microbiome plays a fundamental role in cancer immunity and in determining the efficacy of cancer immunotherapy. In this work, we have investigated whether and to what extent the gut microbiome can influence the antitumor activity of neo-epitope-based cancer vaccines in a BALB/c-CT26 cancer mouse model. Similarly to that observed in the C57BL/6-B16 model, Bifidobacterium administration per se has a beneficial effect on CT26 tumor inhibition. Furthermore, the combination of Bifidobacterium administration and vaccination resulted in a protection which was superior to vaccination alone and to Bifidobacterium administration alone, and correlated with an increase in the frequency of vaccine-specific T cells. The gut microbiome analysis by 16S rRNA gene sequencing and shotgun metagenomics showed that tumor challenge rapidly altered the microbiome population, with Muribaculaceae being enriched and Lachnospiraceae being reduced. Over time, the population of Muribaculaceae progressively reduced while the Lachnospiraceae population increased-a trend that appeared to be retarded by the oral administration of Bifidobacterium. Interestingly, in some Bacteroidales, Prevotella and Muribaculacee species we identified sequences highly homologous to immunogenic neo-epitopes of CT26 cells, supporting the possible role of "molecular mimicry" in anticancer immunity. Our data strengthen the importance of the microbiome in cancer immunity and suggests a microbiome-based strategy to potentiate neo-epitope-based cancer vaccines.
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Project description:Peptide-based cancer vaccines have been shown to boost immune systems to kill tumor cells in cancer patients. However, designing an effective T cell epitope peptide-based cancer vaccine still remains a challenge and is a major hurdle for the application of cancer vaccines. In this study, we constructed for the first time a library of peptide-based cancer vaccines and their clinical attributes, named CancerVaccine (https://peptidecancervaccine.weebly.com/). To investigate the association factors that influence the effectiveness of cancer vaccines, these peptide-based cancer vaccines were classified into high (HCR) and low (LCR) clinical responses based on their clinical efficacy. Our study highlights that modified peptides derived from artificially modified proteins are suitable as cancer vaccines, especially for melanoma. It may be possible to advance cancer vaccines by screening for HLA class II affinity peptides may be an effective therapeutic strategy. In addition, the treatment regimen has the potential to influence the clinical response of a cancer vaccine, and Montanide ISA-51 might be an effective adjuvant. Finally, we constructed a high sensitivity and specificity machine learning model to assist in designing peptide-based cancer vaccines capable of providing high clinical responses. Together, our findings illustrate that a high clinical response following peptide-based cancer vaccination is correlated with the right type of peptide, the appropriate adjuvant, and a matched HLA allele, as well as an appropriate treatment regimen. This study would allow for enhanced development of cancer vaccines.
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