The current role of AI is summarized across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition, and the application of modern computational frameworks.
Abstract
Peptide-based cancer vaccines represent a promising immunotherapeutic strategy aimed at inducing tumor-specific immune responses through the targeting of tumor-associated antigens and neoantigens. Recent advances in next-generation sequencing and immunogenomics have accelerated the identification of candidate neoantigens; however, the development of effective peptide vaccines remains limited by challenges related to antigen selection, HLA polymorphism, antigen processing, and variability in immunogenicity. Artificial intelligence (AI), including machine learning and deep learning approaches, has emerged as a transformative tool capable of addressing these limitations through large-scale integration and analysis of genomic, transcriptomic, proteomic, and immunological data. In this review, we summarize the current role of AI across the peptide cancer vaccine development pipeline, from neoantigen discovery and epitope prioritization to prediction of peptide–HLA binding, antigen presentation, and T-cell receptor recognition. We discuss the application of modern computational frameworks, including pan-allelic prediction models, transformer-based architectures, immunopeptidomics-informed learning, and multi-modal AI systems integrating tumor and immune microenvironment data. Furthermore, we examine the clinical translation of personalized neoantigen vaccines, including their combination with immune checkpoint inhibitors and their emerging role in aggressive malignancies such as glioblastoma. Despite substantial progress, significant challenges remain, including high false-positive prediction rates, limited diversity of training datasets, biological complexity of immunogenicity, and regulatory and manufacturing barriers associated with individualized therapies. Continued integration of AI-driven prediction tools with experimental validation and translational immunology will be essential for the development of clinically effective and scalable precision cancer vaccines.
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
Melanoma, a highly aggressive and therapy-resistant skin cancer, is increasingly treated with immunotherapy, driven by advances in molecular biology and cancer immunology. This study aimed to develop an integrated computational method that combines deep learning based somatic mutation detection, MHC-binding prediction, and reverse vaccinology to identify melanoma neoantigens and design an epitope-based vaccine construct. Paired tumor-normal whole-genome sequencing data were investigated using deep learning-based variant callers, to identify somatic mutations. RNA-seq data were used to validate transcript expression and extract mutant coding sequences. MHC class I binding affinity was evaluated using the network-based deep learning model, and high-affinity binders were assessed using Immunoinformatic filters. Multiple reverse vaccinology filters were then utilized to identify potential neoantigens. A total of 4,050 mutant epitopes were initially predicted, from which nine epitopes met all immunoinformatic selection criteria and were used to construct the multi-epitope vaccine (MEVC). These epitopes were linked using AAY linkers, while a TLR-4 agonist adjuvant was additionally attached via an EAAAK linker to enhance the immunogenicity of the vaccine construct. The final MEVC comprised 145 amino acids, with stable physicochemical properties, signifying improved cellular uptake and immune interaction. Structural modeling, and molecular dynamics simulations of 100-ns confirmed favorable stability and interaction between the vaccine construct and TLR-4. Furthermore, Immune simulation (C-IMMSIM) showed a balanced humoral and cellular immune response. Overall, the results suggest that the proposed MEVC is stable and immunogenic; however, experimental and preclinical validation is required to confirm these findings.
Saba Ismail, Devin Atkin, Khaled Barakat. A Genome-to-Vaccine: An Integrated Deep learning-Driven Neoantigen Identification and Multi-Epitope Vaccine Construction for Personalized Melanoma Immunotherapy [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Breaking Barriers in the Fight against Rare Cancers; 2026 Jul 18-20; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2026;86(14_Suppl):Abstract nr B055.
S. Ismail, Devin Atkin, K. Barakat· Cancer Research· 0 citations
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.
Minglu Ge, Ning Wu· Cancer Treatment and Researc...· 0 citations
This work is the first to use a multi-modal foundation model for neoantigen vaccines, overcoming the tumor-type-specific constraints of conventional approaches and facilitating joint modelling of immunogenicity and clinical efficacy, thereby offering an AI decision engine for precision cancer vaccine design that is applicable to all cancer types.
Gang Liu, Jia Wang, Jia Zhu· Global Health Care· 0 citations
Precision medicine provides a therapeutic framework that addresses the interindividual diversity in tumor genetics, immunological determinants of disease, and immune responsiveness. Among precision approaches, personalized cancer vaccines (PCVs) have gained attention as a promising strategy for inducing tumor-specific immunity by identifying patient-derived neoantigens. Despite encouraging clinical outcomes, the development of PCVs faces major challenges in optimizing antigen selection, delivery, immune activation, and clinical translation. This Review summarizes current advances in PCV research, covering tumor antigen classification, neoantigen prediction algorithms, and the evolution of delivery technologies such as peptide, dendritic cell, DNA, and mRNA-lipid nanoparticle platforms. We highlight the advantages of mRNA-lipid nanoparticle systems that enable rapid manufacturing, potent antigen expression, and integration with immunostimulatory cytokines. Furthermore, we discuss emerging combination strategies involving cytokine engineering and T cell modulation that provide new opportunities to enhance immunogenicity and therapeutic efficacy. Ultimately, we propose that the future maturation of PCVs will require a coordinated process across five interconnected dimensions: (1) advances in neoantigen prediction technologies, (2) rapid delivery of PCVs to patients, (3) an increase in anticancer efficacy through combination therapy strategies, (4) establishment of cost-effective manufacturing processes, and (5) regulatory innovation. In this Review, we conceptualize this multidimensional alignment as "Take Five," a unifying framework to bring precision, rhythm, and balance to next-generation cancer immunotherapy.
Seongje Cho, Jisun Lee, Young-Min Lee et al.· Experimental and Molecular M...· 0 citations