Aug 2026· Cancer Treatment and Research Communications· Vol 48, pp.
101358
· 0 citations· 53 references
Medicine
TL;DR
The strongest current signal supports use in adjuvant, perioperative, and minimal residual disease settings, usually in combination with checkpoint blockade or other immune-modifying strategies, usually in combination with checkpoint blockade or other immune-modifying strategies.
Abstract
Neoantigen vaccines have become a central direction in precision cancer immunotherapy because they aim to target tumor-specific peptide sequences generated by somatic alterations rather than self-antigens shared with normal tissues. This biological distinction reduces the barrier of central tolerance and creates a rational basis for individualized T-cell priming. The field has also changed technically. Tumor-normal sequencing, transcriptomic filtering, HLA typing, immunopeptidomics, and algorithmic prioritization now make it possible to move from a patient tumor sample to a ranked set of candidate vaccine targets within a clinically meaningful interval. Because durable vaccine responses frequently depend on CD4-positive T-cell help, we also give explicit attention to HLA class II prediction, which remains substantially less accurate than class I prediction. Among available delivery formats, mRNA platforms have become especially important because they can encode multiple patient-specific epitopes in a single product and can be redesigned rapidly as prediction and delivery methods improve, although synthetic long peptide, dendritic cell, viral vector, and DNA platforms retain specific advantages that we compare directly. This review re-examines neoantigen vaccines as a translational system rather than as a single technology. We first outline the biological basis of neoantigen recognition and classify the major antigen sources. We then discuss target discovery, HLA-restricted presentation, computational ranking, immunopeptidomic evidence, and functional validation. Next, we compare vaccine platforms, with particular emphasis on why personalized mRNA vaccines now dominate late-stage clinical development. Finally, we analyze current clinical evidence in melanoma, pancreatic ductal adenocarcinoma, renal cell carcinoma, glioblastoma, and other solid tumors-reporting primary efficacy endpoints, hazard ratios, patient numbers, and follow-up durations where available-and we identify the main barriers that still prevent broad clinical implementation. In our assessment-offered as an expert interpretation rather than as a conclusion derived from comparative or pooled analyses, because the supporting evidence still rests largely on single-arm and early-phase trials in heterogeneous tumor types-the strongest current signal supports use in adjuvant, perioperative, and minimal residual disease settings, usually in combination with checkpoint blockade or other immune-modifying strategies. Neoantigen vaccination is unlikely to become a universal standalone therapy. Its more realistic value is as a programmable immune-priming component within precision oncology.
It is proposed that the future maturation of PCVs will require a coordinated process across five interconnected dimensions: advances in neoantigen prediction technologies, rapid delivery of PCVs to patients, an increase in anticancer efficacy through combination therapy strategies, establishment of cost-effective manufacturing processes, and regulatory innovation.
Seongje Cho, Jisun Lee, Young-Min Lee et al.· Experimental and Molecular M...· 0 citations
Neoantigens—tumor-specific peptides generated by somatic mutations—are central targets of effective anticancer T cell immunity and underpin the clinical success of immune checkpoint blockade and personalized cancer vaccines. Advances in high-throughput sequencing, immunopeptidomics, and artificial intelligence (AI) have transformed neoantigen discovery from tailored experimental workflows into scalable, computational pipelines. However, accurately identifying the small subset of tumor mutations that yield processed, presented, and immunogenic epitopes remains a major bottleneck. This review summarizes how AI is reshaping neoantigen discovery, from somatic variant calling, HLA typing, and peptide processing to peptide–MHC binding, presentation, and T cell recognition. We first outline the immunobiological foundations of antigen presentation, emphasizing class I and II peptide-binding grooves and their allele-specific motifs, then describe AI workflows that integrate somatic mutation calling, HLA typing, transcriptomics, and immunopeptidomics to nominate candidate neoepitopes. We highlight recent AI-driven tools for presentation and immunogenicity prediction, integrative pipelines that support personal and shared neoantigen targeting, and early clinical applications in vaccination and T cell therapies. AI-driven models trained on eluted ligand datasets substantially outperform affinity-only predictors for peptide presentation across diverse HLA alleles and populations. Consortium-scale benchmarking demonstrates that integrating features of antigen processing, presentation, and TCR recognition can eliminate the majority of non-immunogenic candidates while retaining clinically relevant neoepitopes. Immunopeptidomics provides essential ground truth, revealing that only a small fraction of genomically predicted candidates are naturally presented and uncovering noncanonical antigen sources, including splice variants, post-translational modifications, and noncoding regions. Integrative pipelines now support both personal (private) and shared (public) neoantigen prioritization, enabling translational applications such as personalized vaccines and TCR-based therapies. AI-guided neoantigen discovery is now clinically actionable, enabled by immunopeptidomics and deep learning models. Despite significant progress, key challenges remain, including limited class II prediction accuracy, incomplete coverage of rare HLA alleles, tumor heterogeneity, and the need for standardized benchmarking and validation. Anchoring computational predictions to mass spectrometry–derived ligands and incorporating tumor evolution and immune escape mechanisms will be critical for improving target selection. Continued integration of AI, proteogenomics, and clinical data is poised to accelerate the development of effective, precision neoantigen-based cancer immunotherapies.
Atefeh Bakhshian, Sajjad Ghorghanlu, Fereshteh Fallah Atanaki et al.· Journal of Translational Med...· 0 citations
Cancer vaccines have resurged in oncology because they address a central question in precision medicine: whether the molecular identity of a tumor can be converted into an immune target that is both specific and clinically useful. Preventive vaccines against oncogenic viruses have already shown that immune intervention can reduce the burden of virus-associated cancers. Therapeutic cancer vaccines face a more difficult task, because established tumors arise from self-tissues, change over time, and often acquire mechanisms that limit antigen presentation, T-cell entry, or immune-mediated killing. This review examines cancer vaccines as biomarker-driven tools within precision oncology. The focus is not only on vaccine platforms, but on the biological requirements that make an antigen suitable for therapeutic targeting. Tumor-specific mutations, viral antigens, recurrent driver alterations, frameshift peptides, cancer-testis antigens, and personalized neoantigens may all provide vaccine targets, but their presence alone is not enough. A clinically relevant vaccine antigen should be expressed by tumor cells, processed and presented through HLA molecules, recognized by functional T cells, and sufficiently retained during tumor evolution. This distinction is particularly important because sequencing and computational prediction now generate many candidate neoantigens whose immunological relevance still requires experimental confirmation. Particular attention is given to antigen-presentation defects, clonal and subclonal heterogeneity, tumor microenvironment barriers, circulating tumor DNA-defined minimal residual disease, and immune-response monitoring. Colorectal cancer is used as a working model because microsatellite instability-high/mismatch repair-deficient tumors, microsatellite-stable tumors with immune-resistant features, and recurrent alterations in MMR genes, KRAS, BRAF, adenomatous polyposis coli, and TP53 illustrate how cancer-cell signaling, neoantigen generation, tumor microenvironment remodeling, and patient stratification intersect. Overall, cancer vaccines are unlikely to become universal stand-alone treatments for advanced solid tumors. Their most credible role may emerge in molecularly selected patients, adjuvant therapy, minimal residual disease, virus-associated malignancies, and rational combinations with checkpoint inhibitors or tumor microenvironment-modulating agents.
Dario Rusciano· Frontiers in Cell and Develo...· 0 citations
mRNA has emerged as a transformative platform in vaccine oncology, offering rapid design, non-acquired security and powerful activation of adaptive immune. Despite encouraging preclinical and clinical results, challenges such as efficient distribution, tumor immune evasion, and patient-specific antigen selection limits their widespread clinical translations. We systematically reviewed preclinical studies and clinical trials examining mRNA vaccines in various cancer, including glioblastoma, melanoma, lungs, breasts, digestive systems, ovarian, prostate, kidney and hematological malignancies. Comparative analysis were carried out between mRNA, DNA and peptide vaccine platforms. Additionally, we critically examined the tumor resistance mechanism and proposed the next generation strategies, including the nanometer-based delivery system, self-ampliming mRNA (saRNA), and bioinformatics-operated antigen discovery, which are using single-cell sequencing. Clinical trials continuously display the safety and immunity of mRNA vaccines, with a strong induction of CD8+ T cell reactions and cancer types. However, efficacy outcomes remain variable, with strongest responses in high-mutation-burden tumors such as melanoma and NSCLC, and limited benefit in low-mutation-burden tumors such as prostate and ovarian cancers. Our ideological structure introduces the integration of mRNA vaccines with car-T cells and monoclonal antibodies to remove tumor immune resistance. In addition, we present an accurate algorithm, taking advantage of scRNA-seq for patient-specific neostagen selection, and highlight the benefits of saRNA in increasing antigen expression with low dosage requirements. mRNA vaccine represents a rapidly growing frontier in cancer immunotherapy, yet their success will depend on addressing biological and translation obstacles. This review synthesizes not only current clinical evidence, but also provides innovative conceptual models and future instructions that may redefine the design of the next generation cancer vaccines. By bridging advances in nanotechnology, computational biology and adaptive clinical trial designs, the task offers a roadmap to achieve sustainable, individual and widely accessible cancer immunotherapy.