Immunotherapeutic Peptide Vaccine Approaches to Glioma

Heretofore, there are no FDA-approved immunotherapeutics for malignant gliomas despite many novel therapies currently in different stages of clinical trials. Malignant gliomas are immunosuppressive tumors and are difficult for immune effector cells to infiltrate the tumor sites in the central nervous system. This inefficiency results in median survival of about only two years with a few long-term survivors. Recent clinical trials of vaccine-based immunotherapies against malignant gliomas have demonstrated encouraging results in enhancing progression-free survival and overall survival of patients. The vaccine-based treatments include peptide and heat-shock proteins, dendritic cell-based vaccines, as well as viral-based immunotherapy. In this review, we will focus on recent clinical trials of neoantigen peptide vaccines on gliomas, the delivery routes of such peptide vaccines, their adjuvants, clinical challenges, and its future strategies, respectively.

S-540956, a CpG Oligonucleotide Annealed to a Complementary Strand With an Amphiphilic Chain Unit, Acts as a Potent Cancer Vaccine Adjuvant by Targeting Draining Lymph Nodes

Robust induction of cancer-antigen-specific CD8+ T cells is essential for the success of cancer peptide vaccines, which are composed of a peptide derived from a cancer-specific antigen and an immune-potentiating adjuvant, such as a Toll-like receptor (TLR) agonist. Efficient delivery of a vaccine antigen and an adjuvant to antigen-presenting cells in the draining lymph nodes (LNs) holds key to maximize vaccine efficacy. Here, we developed S-540956, a novel TLR9-agonistic adjuvant consisting of B-type CpG ODN2006 (also known as CpG7909), annealed to its complementary sequence oligodeoxynucleotide (ODN) conjugated to a lipid; it could target both a cancer peptide antigen and a CpG-adjuvant in the draining LNs. S-540956 accumulation in the draining LNs and activation of plasmacytoid dendritic cells (pDCs) were significantly higher than that of ODN2006. Mechanistic analysis revealed that S-540956 enhanced the induction of MHC class I peptide-specific CD8+ T cell responses via TLR9 in a CD4+ T cell-independent manner. In mice, the therapeutic effect of S-540956-adjuvanted with a human papillomavirus (HPV)-E7 peptide vaccine against HPV-E7-expressing TC-1 tumors was significantly better than that of an ODN2006-adjuvanted vaccine. Our findings demonstrate a novel adjuvant discovery with the complementary strand conjugated to a lipid, which enabled draining LN targeting and increased ODN2006 accumulation in draining LNs, thereby enhancing the adjuvant effect. Our findings imply that S-540956 is a promising adjuvant for cancer peptide vaccines and has a high potential for applications in various vaccines, including recombinant protein vaccines.

Dynamic Covalent Dextran Hydrogels as Injectable, Self-adjuvating Peptide Vaccine Depots

Dextran-based hydrogels are promising therapeutic materials for drug delivery, tissue regeneration devices, and cell therapy vectors, due to their high biocompatibility, along with their ability to protect and release active therapeutic agents. This report describes the synthesis, characterization and application of a new dynamic covalent dextran hydrogel as an injectable depot for peptide vaccines. Dynamic covalent crosslinks based on double Michael addition of thiols to alkynones impart the dextran hydrogel with shear-thinning and self-healing capabilities, enabling hydrogel injection. These injectable, non-toxic hydrogels show adjuvant potential and have predictable sub-millimolar loading and release of the peptide antigen SIINFEKL, which after its release is able to activate T-cells, demonstrating that the hydrogels deliver peptides without modifying their immunogenicity. This work demonstrates the potential of dynamic covalent dextran hydrogels as a sustained-release material for delivery of peptide vaccines.

Peptide vaccines designed with the aid of immunoinformatic against Caseous Lymphadenitis promotes humoral and cellular response induction in mice

Caseous Lymphadenitis (CLA) is a chronic disease that affects also small ruminants. CLA is caused by Corynebacterium pseudotuberculosis and is responsible for high economic losses due to the formation of superficial and visceral granulomas, the latter is considered as asymptomatic CLA causing high levels of dissemination. Several vaccination strategies, in which the use of synthetic peptides stands out. Thus, this work aimed to evaluate the protective potential of peptide vaccines designed to determine the immunodominant epitopes of CP40 against CLA in mice. The animals were divided into eight groups separated in controls (G1—PBS, G2—Saponin and G9—rCP40) and experimental (G3—pep1, G4- pep2, G5-pep3, G6-pep4, G7-pep5 and G8-pep6), these were vaccinated on days 0 and 15 by a subcutaneous route. 60 days after the first immunization, all animals were challenged with C. pseudotuberculosis. On days 0, 15, 60, and 120 after the first immunization, blood samples were taken to measure immunoglobulins. On the same day of the challenge, the splenocytes were isolated and assayed for the production of IL-2, IL-4, IL-6, IFN-γ, TNF-α, IL-17, and IL-10. After vaccinations, the animals were challenged and all of them were affected by the disease which led to their death. The G6 and G8 groups provided 10% protection and the G7 provided 20%. The G3 and G4 groups provided 30% and 40% protection respectively. The peptides showed the production of Total IgG antibodies and cytokines (IL-2, IL-4, IL-6, IFN-γ, and TNF-α), indicating a possible activation of the Th1 type response. However, groups G3, G5, G6, and G8 showed production of IL-17. None of the study groups showed IL-10 production. The immunogenicity of the peptides was not enough to protect these animals and it is believed that the use of adjuvants based on PAMPs may improve the immune response offered by these peptides.

Machine Learning-Based Ensemble Model for Zika Virus T-Cell Epitope Prediction

Zika virus (ZIKV), the causative agent of Zika fever in humans, is an RNA virus that belongs to the genus Flavivirus. Currently, there is no approved vaccine for clinical use to combat the ZIKV infection and contain the epidemic. Epitope-based peptide vaccines have a large untapped potential for boosting vaccination safety, cross-reactivity, and immunogenicity. Though many attempts have been made to develop vaccines for ZIKV, none of these have proved to be successful. Epitope-based peptide vaccines can act as powerful alternatives to conventional vaccines due to their low production cost, less reactogenic, and allergenic responses. For designing an effective and viable epitope-based peptide vaccine against this deadly virus, it is essential to select the antigenic T-cell epitopes since epitope-based vaccines are considered safe. The in silico machine-learning-based approach for ZIKV T-cell epitope prediction would save a lot of physical experimental time and efforts for speedy vaccine development compared to in vivo approaches. We hereby have trained a machine-learning-based computational model to predict novel ZIKV T-cell epitopes by employing physicochemical properties of amino acids. The proposed ensemble model based on a voting mechanism works by blending the predictions for each class (epitope or nonepitope) from each base classifier. Predictions obtained for each class by the individual classifier are summed up, and the class with the majority vote is predicted upon. An odd number of classifiers have been used to avoid the occurrence of ties in the voting. Experimentally determined ZIKV peptide sequences data set was collected from Immune Epitope Database and Analysis Resource (IEDB) repository. The data set consists of 3,519 sequences, of which 1,762 are epitopes and 1,757 are nonepitopes. The length of sequences ranges from 6 to 30 meter. For each sequence, we extracted 13 physicochemical features. The proposed ensemble model achieved sensitivity, specificity, Gini coefficient, AUC, precision, F-score, and accuracy of 0.976, 0.959, 0.993, 0.994, 0.989, 0.985, and 97.13%, respectively. To check the consistency of the model, we carried out five-fold cross-validation and an average accuracy of 96.072% is reported. Finally, a comparative analysis of the proposed model with existing methods has been carried out using a separate validation data set, suggesting the proposed ensemble model as a better model. The proposed ensemble model will help predict novel ZIKV vaccine candidates to save lives globally and prevent future epidemic-scale outbreaks.

Peptides Derived From S and N Proteins of Severe Acute Respiratory Syndrome Coronavirus 2 Induce T Cell Responses: A Proof of Concept for T Cell Vaccines

The emergence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants that escape vaccine-induced neutralizing antibodies has indicated the importance of T cell responses against this virus. In this study, we highlight the SARS-CoV-2 epitopes that induce potent T cell responses and discuss whether T cell responses alone are adequate to confer protection against SARS-CoV-2 and describe the administration of 20 peptides with an RNA adjuvant in mice. The peptides have been synthesized based on SARS-CoV-2 spike and nucleocapsid protein sequences. Our study demonstrates that immunization with these peptides significantly increases the proportion of effector memory T cell population and interferon-γ (IFN-γ)-, interleukin-4 (IL-4)-, tumor necrosis factor-α (TNF-α)-, and granzyme B-producing T cells. Of these 20 peptides, four induce the generation of IFN-γ-producing T cells, elicit CD8+ T cell (CTL) responses in a dose-dependent manner, and induce cytotoxic T lymphocytes that eliminate peptide-pulsed target cells in vivo. Although it is not statistically significant, these peptide vaccines reduce viral titers in infected hamsters and alleviate pulmonary pathology in SARS-CoV-2-infected human ACE2 transgenic mice. These findings may aid the design of effective SARS-CoV-2 peptide vaccines, while providing insights into the role of T cells in SARS-CoV-2 infection.