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The current epitope selection methods for peptide vaccines often rely on epitope binding affinity predictions, prompting the need for the development of more sophisticated in silico methods to determine immunologically relevant epitopes. Here, we developed AutoPepVax to expedite and improve the in silico epitope selection for peptide vaccine design. AutoPepVax is a novel program that automatically identifies non-toxic and non-allergenic epitopes capable of inducing tumor-infiltrating lymphocytes by considering various epitope characteristics. AutoPepVax employs random forest classification and linear regression machine-learning-based models, which are trained with datasets derived from tumor samples. AutoPepVax, along with documentation on how to run the program, is freely available on GitHub. We used AutoPepVax to design a pan-cancer peptide vaccine targeting epidermal growth factor receptor (EGFR) missense mutations commonly found in lung adenocarcinoma (LUAD), colorectal adenocarcinoma (CRAD), glioblastoma multiforme (GBM), and head and neck squamous cell carcinoma (HNSCC). These mutations have been previously targeted in clinical trials for EGFR-specific peptide vaccines in GBM and LUAD, and they show promise but lack demonstrated clinical efficacy. Using AutoPepVax, our analysis of 96 EGFR mutations identified 368 potential MHC-I-restricted epitope–HLA pairs from 49,113 candidates and 430 potential MHC-II-restricted pairs from 168,669 candidates. Notably, 19 mutations presented viable epitopes for MHC I and II restrictions. To evaluate the potential impact of a pan-cancer vaccine composed of these epitopes, we used our program, PCOptim, to curate a minimal list of epitopes with optimal population coverage. The world population coverage of our list ranged from 81.8% to 98.5% for MHC Class II and Class I epitopes, respectively. From our list of epitopes, we constructed 3D epitope–MHC models for six MHC-I-restricted and four MHC-II-restricted epitopes, demonstrating their epitope binding potential and interaction with T-cell receptors. AutoPepVax's comprehensive approach to in silico epitope selection addresses vaccine safety, efficacy, and broad applicability. Future studies aim to validate the AutoPepVax-designed vaccines with murine tumor models that harbor the studied mutations. [ABSTRACT FROM AUTHOR]

Acute myeloid leukemia (AML) is a leading blood cancer subtype that can be caused by 27 gene mutations. Previous studies have explored potential vaccine and drug treatments against AML, but many were proven immunologically insignificant. Here, we targeted this issue and applied various clinical filters to improve immune response. KIT is an oncogenic gene that can cause AML when mutated and is predicted to be a promising vaccine target because of its immunogenic responses when activated. We designed a multi-epitope vaccine targeting mutations in the KIT oncogene using CD8+ and CD4+ epitopes. We selected the most viable vaccine epitopes based on thresholds for percentile rank, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, allergenicity, and stability. The efficacy of data was observed through world and regional population coverage of our vaccine design. Then, we obtained epitopes for optimized population coverage from PCOptim-CD, a modified version of our original Java-based program code PCOptim. Using 24 mutations on the KIT gene, 12 CD8+ epitopes and 21 CD4+ epitopes were obtained. The CD8+ dataset had a 98.55% world population coverage, while the CD4+ dataset had a 65.14% world population coverage. There were five CD4+ epitopes that overlapped with the top CD8+ epitopes. Strong binding to murine MHC molecules was found in four CD8+ and six CD4+ epitopes, demonstrating the feasibility of our results in preclinical murine vaccine trials. We then created three-dimensional (3D) models to visualize epitope–MHC complexes and TCR interactions. The final candidate is a non-toxic and non-allergenic multi-epitope vaccine against KIT mutations that cause AML. Further research would involve murine trials of the vaccine candidates on tumor cells causing AML. [ABSTRACT FROM AUTHOR]

We developed an epitope selection method for the design of MHC targeting peptide vaccines. The method utilizes predictions for several clinical checkpoint filters, including binding affinity, immunogenicity, antigenicity, half-life, toxicity, IFNγ release, and instability. The accuracy of the prediction tools for these filter variables was confirmed using experimental data obtained from the Immune Epitope Database (IEDB). We also developed a graphical user interface computational tool called 'PCOptim' to assess the success of an epitope filtration method. To validate the filtration methods, we used a large data set of experimentally determined, immunogenic SARS-CoV-2 epitopes, which were obtained from a meta-analysis. The validation process proved that placing filters on individual parameters was the most effective method to select top epitopes. For a proof-of-concept, we designed epitope-based vaccine candidates for squamous cell carcinoma, selected from the top mutated epitopes of the HRAS gene. By comparing the filtered epitopes to PCOptim's output, we assessed the success of the epitope selection method. The top 15 mutations in squamous cell carcinoma resulted in 16 CD8 epitopes which passed the clinical checkpoints filters. Notably, the identified HRAS epitopes are the same as the clinical immunogenic HRAS epitope-based vaccine candidates identified by the previous studies. This indicates further validation of our filtration method. We expect a similar turn-around for the other designed HRAS epitopes as a vaccine candidate for squamous cell carcinoma. Furthermore, we obtained a world population coverage of 89.45% for the top MHC Class I epitopes and 98.55% population coverage in the absence of the IFNγ release clinical checkpoint filter. We also identified some of the predicted human epitopes to be strong binders to murine MHC molecules, which provides insight into studying their immunogenicity in preclinical models. Further investigation in murine models could warrant the application of these epitopes for treatment or prevention of squamous cell carcinoma. [ABSTRACT FROM AUTHOR]