Neoantigen vaccines are a promising strategy in cancer immunotherapy that leverage tumor-specific mutations to elicit targeted immune responses. Although they have considerable potential, development challenges related to antigen prediction accuracy, manufacturing complexity, and scalability remain key obstacles to their widespread clinical use. This literature review was conducted using PubMed, Scopus, Web of Science, and Google Scholar databases to identify relevant studies. Keywords included “neoantigen vaccines,” “personalized cancer immunotherapy,” “tumor heterogeneity,” “bioinformatics pipelines,” and “prediction algorithms”. Clinical trial data were sourced from ClinicalTrials.gov, Trialtrove, and other publicly available registries. Eligible studies included peer-reviewed research articles, systematic reviews, and clinical trials focusing on neoantigen vaccine development, bioinformatic strategies, and immunotherapy. Tumor heterogeneity and clonal evolution significantly impact vaccine efficacy, necessitating multi-epitope targeting and adaptive vaccine design. Current neoantigen prediction algorithms suffer from high false-positive and false-negative rates, requiring further integration with multi-omics data and machine learning to enhance accuracy. Manufacturing remains complex, time-intensive, and costly, necessitating advancements in standardization and automation. Combination therapies, such as immune checkpoint inhibitors and adoptive cell therapies, counteract the immunosuppressive tumor microenvironment, improving treatment outcomes. Neoantigen vaccines hold great potential for personalized cancer therapy but require advancements in bioinformatics, manufacturing scalability, and immunomodulatory strategies to enhance clinical efficacy. Continued research and interdisciplinary collaboration are essential for refining clinical applications.

Aim: Immunization with meningococcal vaccine (MV) is the most effective measure to control and prevent the transmission of meningococcal infections. In this study, in order to support the appropriate use of various MVs in the prevention of meningococcal meningitis (MM), the effects of MVs, especially single-dose and inter-booster administered, on inflammatory parameters in < 5-year-old children were investigated. Methods: A total of 464 healthy children were included in this study. The data of those who received the first 2 doses at 2-month intervals and the next dose between 8–12 months were included. Nimenrix® (Pfizer) administered as a single dose to children from 12 months of age. Bexsero® (GSK) was administered as 2 + 1 doses under 2 years of age and 2 doses 2 months apart over 2 years of age. Neutrophil, lymphocyte, monocyte, platelet counts, C-reactive protein (CRP), neutrophil-to-lymphocyte ratio (NLR), derived NLR (dNLR), platelet-to-lymphocyte ratio (PLR), systemic inflammation response index (SIR-I), and systemic immune inflammation index (SII) were evaluated. Results: Of the 464 participants, 58.2% were male, with a mean age of 3.81 years, and both sex ratios and ages were similar across the Nimenrix and Bexsero groups. The laboratory and inflammatory parameters of the two vaccine groups were similar. In both vaccine groups, changes in laboratory parameters before and 3-months after vaccination were similar. The changes in laboratory parameters over time between vaccine groups and their interactions were not significant. Conclusions: The NLR, dNLR, PLR, SIR-I, and SII are useful biomarkers indicating the inflammatory response of Nimenrix and Bexsero vaccines. Inflammatory markers can be used as both a safety endpoint and a protection endpoint for MVs (Nimenrix and Bexsero). However, further studies involving larger patient cohorts as well as detailed laboratory data on specific markers of inflammation are needed to draw comprehensive conclusions regarding the inflammatory response following vaccination.