Neoantigen-specific mRNA/DC vaccines for effective anticancer immunotherapy
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A capless hairpin-protected mRNA vaccine encoding the full-length Influenza A hemagglutinin protects mice against a lethal Influenza A infection
The success of mRNA vaccines in controlling the COVID 19 pandemic has confirmed the efficacy of synthetically synthesized mRNA in humans and has also provided a blueprint on how to design them in terms of molecular structure and cost. We describe a mRNA vector that, unlike linear mRNAs used in current vaccines/therapeutics, does not require a 5′ cap to function. The described mRNA vector initiates translation from an internal ribosomal entry site (IRES) and contains specially designed self-folding secondary structures (hairpins) to protect the 5′ end against degradation, dramatically improving its stability. The produced mRNA did not require any additional modifications for functionality. The 5′ hairpins completely inhibited cap-dependent translation, and all vectors containing them required an IRES to express protein. When this capless mRNA vector was constructed to express the full-length Influenza A membrane protein hemagglutinin (HA), complexed with pre-formed lipid-based nanoparticles, and then injected into mice as a vaccine, it generated high titers of anti-HA antibodies and protected mice against a lethal dose of Influenza A.
The guided fire from within: intratumoral administration of mRNA-based vaccines to mobilize memory immunity and direct immune responses against pathogen to target solid tumors
We investigated a novel cancer immunotherapy strategy that effectively suppresses tumor growth in multiple solid tumor models and significantly extends the lifespan of tumor-bearing mice by introducing pathogen antigens into tumors via mRNA-lipid nanoparticles. The pre-existing immunity against the pathogen antigen can significantly enhance the efficacy of this approach. In mice previously immunized with BNT162b2, an mRNA-based COVID-19 vaccine encoding the spike protein of the SARS-CoV-2 virus, intratumoral injections of the same vaccine efficiently tagged the tumor cells with mRNA-expressed spike protein. This action rapidly mobilized the pre-existing memory immunity against SARS-CoV-2 to kill the cancer cells displaying the spike protein, while concurrently reprogramming the tumor microenvironment (TME) by attracting immune cells. The partial elimination of tumor cells in a normalized TME further triggered extensive tumor antigen-specific T cell responses through antigen spreading, eventually resulting in potent and systemic tumor-targeting immune responses. Moreover, combining BNT162b2 treatment with anti-PD-L1 therapy yielded a more substantial therapeutic impact, even in “cold tumor” types that are typically less responsive to treatment. Given that the majority of the global population has acquired memory immunity against various pathogens through infection or vaccination, we believe that, in addition to utilizing the widely held immune memory against SARS-CoV-2 via COVID-19 vaccine, mRNA vaccines against other pathogens, such as Hepatitis B Virus (HBV), Common Human Coronaviruses (HCoVs), and the influenza virus, could be rapidly transitioned into clinical use and holds great promise in treating different types of cancer. The extensive selection of pathogen antigens expands therapeutic opportunities and may also overcome potential drug resistance.
Bridging gene therapy and next-generation vaccine technologies
Contributions of the COVID-19 pandemic to gene-based vaccination Dealing with the serious problems of COVID-19 has propelled vaccine research and the importance of pandemic preparedness…
Sequential STING and CD40 agonism drives massive expansion of tumor-specific T cells in liposomal peptide vaccines
The clinical use of cancer vaccines is hampered by the low magnitude of induced T-cell responses and the need for repetitive antigen stimulation. Here, we demonstrate that liposomal formulations with incorporated STING agonists are optimally suited to deliver peptide antigens to dendritic cells in vivo and to activate dendritic cells in secondary lymphoid organs. One week after liposomal priming, systemic administration of peptides and a costimulatory agonistic CD40 antibody enables ultrarapid expansion of T cells, resulting in massive expansion of tumor-specific T cells in the peripheral blood two weeks after priming. In the MC-38 colon cancer model, this synthetic prime-boost regimen induces rapid regression and cure of large established subcutaneous cancers via the use of a single tumor-specific neoantigen. These experiments demonstrate the feasibility of liposome-based heterologous vaccination regimens to increase the therapeutic efficacy of peptide vaccines in the context of immunogenic adjuvants and costimulatory booster immunizations. Our results provide a rationale for the further development of modern liposomal peptide vaccines for cancer therapy.
A unified deep framework for peptide–major histocompatibility complex–T cell receptor binding prediction
Antigen peptides that are presented by a major histocompatibility complex (MHC) and recognized by a T cell receptor (TCR) have an essential role in immunotherapy. Although substantial progress has been made in predicting MHC presentation, accurately predicting the binding interactions between antigen peptides, MHCs and TCRs remains a major computational challenge. In this paper, we propose a unified deep framework (called UniPMT) for peptide, MHC and TCR binding prediction to predict the binding between the peptide and the CDR3 of TCR β in general, presented by class I MHCs. UniPMT is comprehensively validated by a series of experiments and achieved state-of-the-art performance in the peptide–MHC–TCR, peptide–MHC and peptide–TCR binding prediction tasks with up to 15% improvements in area under the precision–recall curve taking the peptide–MHC–TCR binding prediction task as an example. In practical applications, UniPMT shows strong predictive power, correlates well with T cell clonal expansion and outperforms existing methods in neoantigen-specific binding prediction with up to 17.62% improvements in area under the precision–recall curve on experimentally validated datasets. Moreover, UniPMT provides interpretable insights into the identification of key binding sites and the quantification of peptide–MHC–TCR binding probabilities. In summary, UniPMT shows great potential to serve as a useful tool for antigen peptide discovery, disease immunotherapy and neoantigen vaccine design.
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