Abstract

Abstract Tumor neoantigens, arising from genomic mutations during cancer cell proliferation, are absent in normal tissues, making them ideal immunotherapy targets. Predicting neoantigen peptide sequences using artificial neural networks (ANNs) like NetMHCpan offers a promising approach to developing personalized peptide cancer vaccines. However, limitations such as low affinity, short half-life, and weak immunogenicity hinder their efficacy. To address these challenges, we propose loading neoantigens into nanoparticles for protection and slow release within the body. We used ANN prediction to determine neoantigen peptide sequences for various cancer cell lines and engineered these peptides by conjugating them with Toll-like receptor 7 agonist (TLR7a) adjuvants, forming a self-assembling peptide conjugates nanoparticle (SPCN) platform. To assess the immunogenicity of the SPCN platform, we conducted enzyme-linked ELISpot assays and evaluated the activation of antigen-presenting cells (APCs) in draining lymph nodes using flow cytometry analysis. The therapeutic efficacy of the SPCN platform was determined by monitoring tumor growth curves in mouse models. The SPCN platform demonstrated broad applicability, carrying different neoantigens in vivo, resulting in robust and safe systemic anti-tumor responses. The SPCN enhanced immunogenicity, stimulating splenocytes to produce more interferon gamma and facilitating efficient absorption by antigen-presenting cells (APCs). Moreover, SPCNs loaded with neoantigens targeted different cancer models, including B16OVA, MB49, and RIL175. In conclusion, our self-assembling peptide conjugates nanoparticle platform enhances APC activation and neoantigen immunogenicity, leading to improved anti-tumor efficacy, offering valuable insights for developing synthetic peptide cancer vaccines. Citation Format: Yangfan Wu, Jian-Dong Huang. Enhancing neoantigen cancer vaccine efficacy with a self-assembling peptide conjugates nanoparticle platform [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 499.

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