Aug 2026· Global Health Care· Vol 2, pp. 1-26· 0 citations· 21 references
TL;DR
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.
Abstract
Neoantigen vaccines are a key component of personalised cancer immunotherapy; however, existing prediction techniques are primarily restricted to a single tumour type and have difficulty integrating multi-modal biological data, which leads to inadequate accuracy in immunogenicity evaluation and vaccine efficacy prediction. In order to predict neoantigen immunogenicity and customised vaccination clinical outcomes across various tumour types, this study attempts to develop and verify a universal multi-modal foundation model.Neoantigen peptide sequences, mass spectrometry-derived pHLA binding patterns, single-cell TCR repertoires, tumour transcriptomes, and clinical vaccination trial follow-up records were among the extensive paired data we gathered from 15 solid tumour types. We present the NeoVAX-FM multi-modal foundation model, which first embeds peptide sequences, pHLA complex 3D structures, and gene expression into a single semantic space using a contrastive language-image pretraining paradigm. It is then fine-tuned on downstream tasks to concurrently score immunogenicity and predict progression-free survival.The model was assessed in one prospective clinical trial and three external validation cohorts. NeoVAX-FM showed strong performance in melanoma, non-small cell lung cancer, and microsatellite stable colorectal cancer, with an average AUC of 0.94 for cross-tumor neoantigen immunogenicity prediction—a 12.3% increase over the best currently available techniques. Patients in the prospective vaccination cohort who were projected by the model to be “high responders” had a considerably higher median progression-free survival (HR = 0.28, p < 0.001), and the model was successful in identifying tumour microenvironment characteristics and universal TCR motifs that drive long-term responses.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.
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.
Minglu Ge, Ning Wu· Cancer Treatment and Researc...· 0 citations
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.
P. Brlek, Jan Kolić, L. Bulić et al.· Frontiers in Genetics· 0 citations
The ImmunoFoundation Model (IFM), a multimodal deep learning system that integrates not only peptide sequences, 3D molecular structures, and biochemical properties but also TCR-MHC-peptide to achieve superior immunogenicity prediction and enable peptide optimization for therapeutic applications is developed.
Smita Krishnaswamy, J. Rocha, Hiren Madhu et al.· Journal of Immunology· 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
Understanding vaccine durability is key to designing immunizations with long-term efficacy. Leveraging the Immune Signatures Data Resource, a compendium of transcriptomic and immunological responses from 1405 healthy adults (18+ years) across 24 vaccines, we investigated shared immune mechanisms underlying durable antibody responses. The dataset spans live (yellow fever, smallpox), recombinant viral-vector (Ebola), inactivated (influenza), and glycoconjugate (pneumococcal) vaccines.
Data preprocessing included imputation, normalization, and alignment across post-vaccination time points. We applied advanced machine learning (ML) frameworks to predict antibody immunogenicity and durability. Feature selection for high-dimensional, low-sample-size multi-omics datasets was performed using HSIC Lasso to identify predictors of antibody responses. Selected features served as input to ensemble, regularized regression, and gradient-boosting models (e.g. DT, RF, LASSO, XGB, CatBoost). We compared single-target and multi-output approaches, evaluating stacked, chained, and wrapper-based strategies, and implemented multi-layer neural networks to capture complex relationships among immune features.
Post-vaccination time points explained ∼15% of the total variance, indicating shared immune kinetics across vaccine types, while age and sex contributed minimally. Gradient-boosting and multi-output modeling approaches achieved the highest predictive accuracy across vaccines, highlighting the value of integrating correlated outcomes. Neural network models similarly captured complex, nonlinear immune signatures, albeit with reduced explainability.
Conserved transcriptional modules, particularly interferon-signaling and plasmablast-related pathways, emerged as strong predictors of antibody durability. This integrative ML framework enables identification of key immune signatures critical for developing vaccines with durable responses, advancing data-driven strategies for systems vaccinology.
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Computational and Systems Immunology (COMP)
Marco Sanna, Jing Chen, Paolo Palma et al.· Journal of Immunology· 0 citations
