Abstract
Abstract Antitumor peptide vaccine is an alternative and attractive way to eradicate tumors by the aid of host immune system. The current peptide vaccine development is hampered by several hurdles. These include heterogeneity of tumor cells, applicable in vivo stability of peptides, and others. One of the major obstacles is the precise prediction of cancer antigen, which can be loaded efficiently onto antigen presenting cells. We present here a neoantigen prediction algorithm DeepNeo comprised of DeepNeo-MHC and DeepNeo-TCR, which calculates the antigenicity of neoantigen based on the MHC binding and TCR activation potential. We examined the efficacy of DeepNeo using ELISPOT and two allogenic mouse tumor models. By treating the combination of long peptides targeting MC38 colon or B16F10 melanoma cell lines, we have shown the DeepNeo effectively determines neoantigens for the development of antitumor peptide vaccine. Analysis of tumor-infiltrated lymphocytes (TIL) supported our results. Undergoing study using patient-derived xenograft model treated with autologous, trained PBMC with personalized peptide vaccine will be discussed further. Citation Format: Eun Ji Lee, Min Ji Park, Soo Jin Kim, Seung-Jae Noh, Inkyung Shin, Jeong Yeon Kim, Incheol Shin, Jung Kyoon Choi, Dae-Yeon Cho, Suhwan Chang. Efficacy analysis of the DeepNeo neoantigen prediction tool for antitumor vaccine development [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB168.
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