Browse
Submit Data
Databases
API
Help

Dataset Information

10 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Targeting Cross-Presentation as a Route to Improve the Efficiency of Peptide-Based Cancer Vaccines.

ABSTRACT: Cross-presenting dendritic cells (DC) offer an attractive target for vaccination due to their unique ability to process exogenous antigens for presentation on MHC class I molecules. Recent reports have established that these DC express unique surface receptors and play a critical role in the initiation of anti-tumor immunity, opening the way for the development of vaccination strategies specifically targeting these cells. This study investigated whether targeting cross-presenting DC by two complementary mechanisms could improve vaccine effectiveness, in both a viral setting and in a murine melanoma model. Our novel vaccine construct contained the XCL1 ligand, to target uptake to XCR1⁺ cross-presenting DC, and a cell penetrating peptide (CPP) with endosomal escape properties, to enhance antigen delivery into the cross-presentation pathway. Using a prime-boost regimen, we demonstrated robust expansion of antigen-specific T cells following vaccination with our CPP-linked peptide vaccine and protective immunity against HSV-1 skin infection, where vaccine epitopes were natively expressed by the virus. Additionally, our novel vaccination strategy slowed tumor outgrowth in a B16 murine melanoma model, compared to adjuvant only controls, suggesting antigen-specific anti-tumor immunity was generated following vaccination. These findings suggest that novel strategies to target the antigen cross-presentation pathway in DC may be beneficial for the generation of anti-tumor immunity.

SUBMITTER: Wylie B

PROVIDER: S-EPMC8699136 | biostudies-literature |

REPOSITORIES: biostudies-literature

ACCESS DATA

Json Xml

Similar Datasets

The route of administration dictates the immunogenicity of peptide-based cancer vaccines in mice.

Project description:Vaccines consisting of synthetic peptides representing cytotoxic T-lymphocyte (CTL) epitopes have long been considered as a simple and cost-effective approach to treat cancer. However, the efficacy of these vaccines in the clinic in patients with measurable disease remains questionable. We believe that the poor performance of peptide vaccines is due to their inability to generate sufficiently large CTL responses that are required to have a positive impact against established tumors. Peptide vaccines to elicit CTLs in the clinic have routinely been administered in the same manner as vaccines designed to induce antibody responses: injected subcutaneously and in many instances using Freund's adjuvant. We report here that peptide vaccines and poly-ICLC adjuvant administered via the unconventional intravenous route of immunization generate substantially higher CTL responses as compared to conventional subcutaneous injections, resulting in more successful antitumor effects in mice. Furthermore, amphiphilic antigen constructs such as palmitoylated peptides were shown to be better immunogens than long peptide constructs, which now are in vogue in the clinic. The present findings if translated into the clinical setting could help dissipate the wide-spread skepticism of whether peptide vaccines will ever work to treat cancer.

| S-EPMC6428613 | biostudies-literature

Prioritizing candidate peptides for cancer vaccines through predicting peptide presentation by HLA-I proteins.

Project description:Cancer (treatment) vaccines that are made of neoantigens, or peptides unique to tumor cells due to somatic mutations, have emerged as a promising method to reinvigorate the immune response against cancer. A key step to prioritizing neoantigens for cancer vaccines is computationally predicting which neoantigens are presented on the cell surface by a human leukocyte antigen (HLA). We propose to address this challenge by training a neural network using mass spectrometry (MS) data composed of peptides presented by at least one of several HLAs of a subject. We embed the neural network within a mixture model and train the neural network by maximizing the likelihood of the mixture model. After evaluating our method using data sets where the peptide presentation status was known, we applied it to analyze somatic mutations of 60 melanoma patients and identified a group of neoantigens more immunogenic in tumor cells than in normal cells. Moreover, neoantigen burden estimated by our method was significantly associated with a measurement of the immune system activity, suggesting these neoantigens could induce an immune response.

| S-EPMC10548401 | biostudies-literature

Peptide-based synthetic vaccines.

Project description:Classically all vaccines were produced using live or attenuated microorganisms or parts of them. However, the use of whole organisms, their components or the biological process for vaccine production has several weaknesses. The presence of immunologically redundant biological components or biological impurities in such vaccines might cause major problems. All the disadvantageous of traditional vaccines might be overcome via the development of fully synthetic peptide-based vaccines. However, once minimal antigenic epitopes only are applied for immunisation, the immune responses are poor. The use of an adjuvant can overcome this obstacle; however, it may raise new glitches. Here we briefly summarise the current stand on peptide-based vaccines, discuss epitope and adjuvant design, and multi-epitope and nanoparticle-based vaccine approaches. This mini review discusses also the disadvantages and benefits associated with peptide-based vaccines. It proposes possible methods to overcome the weaknesses of the synthetic vaccine strategy and suggests future directions for its development.

| S-EPMC5529997 | biostudies-other

Approaches to Improve Chemically Defined Synthetic Peptide Vaccines.

Project description:Progress made in peptide-based vaccinations to induce T-cell-dependent immune responses against cancer has invigorated the search for optimal vaccine modalities. Design of new vaccine strategies intrinsically depends on the knowledge of antigen handling and optimal epitope presentation in both major histocompatibility complex class I and -II molecules by professional antigen-presenting cells to induce robust CD8 and CD4 T-cell responses. Although there is a steady increase in the understanding of the underlying mechanisms that bridges innate and adaptive immunology, many questions remain to be answered. Moreover, we are in the early stage of exploiting this knowledge to clinical advantage. Several adaptations of peptide-based vaccines like peptide-adjuvant conjugates have been explored and showed beneficial outcomes in preclinical models; but in the clinical trials conducted so far, mixed results were obtained. A major limiting factor to unravel antigen handling mechanistically is the lack of tools to efficiently track peptide vaccines at the molecular and (sub)cellular level. In this mini-review, we will discuss options to develop molecular tools for improving, as well as studying, peptide-based vaccines.

| S-EPMC5932164 | biostudies-literature

Beyond Just Peptide Antigens: The Complex World of Peptide-Based Cancer Vaccines.

Project description:Peptide-based cancer vaccines rely upon the strong activation of the adaptive immune response to elicit its effector function. They have shown to be highly specific and safe, but have yet to prove themselves as an efficacious treatment for cancer in the clinic. This is for a variety of reasons, including tumour heterogeneity, self-tolerance, and immune suppression. Importance has been placed on the overall design of peptide-based cancer vaccines, which have evolved from simple peptide derivatives of a cancer antigen, to complex drugs; incorporating overlapping regions, conjugates, and delivery systems to target and stimulate different components of antigen presenting cells, and to bolster antigen cross-presentation. Peptide-based cancer vaccines are increasingly becoming more personalised to an individual's tumour antigen repertoire and are often combined with existing cancer treatments. This strategy ultimately aids in combating the shortcomings of a more generalised vaccine strategy and provides a comprehensive treatment, taking into consideration cancer cell variability and its ability to avoid immune interrogation.

| S-EPMC8279810 | biostudies-literature

Epitope targeting with self-assembled peptide vaccines.

Project description:Nanoparticle-based delivery systems are being used to simplify and accelerate new vaccine development. Previously, we described the solid-phase synthesis of a 61-amino acid conjugate vaccine carrier comprising a α-helical domain followed by two universal T cell epitopes. Circular dichroism, analytical centrifugation, and dynamic light scattering indicate that this carrier forms coiled-coil nanoparticles. Here we expand the potential of this carrier by appending B cell epitopes to its amino acid sequence, thereby eliminating the need for traditional conjugation reactions. Peptides containing Tau or amyloid-β epitopes at either terminus assemble into ~20 nm particles and induce antibody responses in outbred mice. Vaccine function was verified in three experiments. The first targeted gonadotropin-releasing hormone, a 10-amino acid neuropeptide that regulates sexual development. Induction of peak antibody titers in male mice stimulated a dramatic loss in fertility and marked testis degeneration. The second experiment generated antibodies to an epitope on the murine IgE heavy chain analogous to human IgE sequence recognized by omalizumab, the first monoclonal antibody approved for the treatment of allergic asthma. Like omalizumab, the anti-IgE antibodies in immunized mice reduced the concentrations of circulating free IgE and prevented IgE-induced anaphylaxis. Finally, a peptide containing the highly conserved Helix A epitope within the influenza hemagglutinin stem domain induced antibodies that successfully protected mice against a lethal H1N1 challenge. These results establish the utility of a new vaccine platform for eliciting prophylactic and therapeutic antibodies to linear and helical B cell epitopes.

| S-EPMC6642127 | biostudies-literature

Potential association factors for developing effective peptide-based cancer vaccines.

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.

| S-EPMC9364268 | biostudies-literature

Protein-based antigen presentation platforms for nanoparticle vaccines.

Project description:Modern vaccine design has sought a minimalization approach, moving to the isolation of antigens from pathogens that invoke a strong neutralizing immune response. This approach has created safer vaccines but may limit vaccine efficacy due to poor immunogenicity. To combat global diseases such as COVID-19, malaria, and AIDS there is a clear urgency for more effective next-generation vaccines. One approach to improve the immunogenicity of vaccines is the use of nanoparticle platforms that present a repetitive array of antigen on its surface. This technology has been shown to improve antigen presenting cell uptake, lymph node trafficking, and B-cell activation through increased avidity and particle size. With a focus on design, we summarize natural platforms, methods of antigen attachment, and advancements in generating self-assembly that have led to new engineered platforms. We further examine critical parameters that will direct the usage and development of more effective platforms.

| S-EPMC8119681 | biostudies-literature

Peptide-Based Vaccines for Tuberculosis.

Project description:Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis. As a result of the coronavirus disease 2019 (COVID-19) pandemic, the global TB mortality rate in 2020 is rising, making TB prevention and control more challenging. Vaccination has been considered the best approach to reduce the TB burden. Unfortunately, BCG, the only TB vaccine currently approved for use, offers some protection against childhood TB but is less effective in adults. Therefore, it is urgent to develop new TB vaccines that are more effective than BCG. Accumulating data indicated that peptides or epitopes play essential roles in bridging innate and adaptive immunity and triggering adaptive immunity. Furthermore, innovations in bioinformatics, immunoinformatics, synthetic technologies, new materials, and transgenic animal models have put wings on the research of peptide-based vaccines for TB. Hence, this review seeks to give an overview of current tools that can be used to design a peptide-based vaccine, the research status of peptide-based vaccines for TB, protein-based bacterial vaccine delivery systems, and animal models for the peptide-based vaccines. These explorations will provide approaches and strategies for developing safer and more effective peptide-based vaccines and contribute to achieving the WHO's End TB Strategy.

| S-EPMC8841753 | biostudies-literature

Trial Watch: Peptide-based anticancer vaccines.

Project description:Malignant cells express antigens that can be harnessed to elicit anticancer immune responses. One approach to achieve such goal consists in the administration of tumor-associated antigens (TAAs) or peptides thereof as recombinant proteins in the presence of adequate adjuvants. Throughout the past decade, peptide vaccines have been shown to mediate antineoplastic effects in various murine tumor models, especially when administered in the context of potent immunostimulatory regimens. In spite of multiple limitations, first of all the fact that anticancer vaccines are often employed as therapeutic (rather than prophylactic) agents, this immunotherapeutic paradigm has been intensively investigated in clinical scenarios, with promising results. Currently, both experimentalists and clinicians are focusing their efforts on the identification of so-called tumor rejection antigens, i.e., TAAs that can elicit an immune response leading to disease eradication, as well as to combinatorial immunostimulatory interventions with superior adjuvant activity in patients. Here, we summarize the latest advances in the development of peptide vaccines for cancer therapy.

| S-EPMC4485775 | biostudies-literature

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data