Neoantigen Identification and Clinical Translation in Cancer Immunotherapy: Technologies, Challenges, and Future Directions

Abstract

The cancer treatment landscape has shifted from traditional chemotherapy and radiotherapy to immunotherapy, which involves modifying the tumor microenvironment to stimulate the patient's immune system. Cancer immunotherapy has transformed the treatment options for a wide range of malignancies. Unlike traditional therapies, neoantigen-targeted Immunotherapy seek to use the immune system's specificity, providing personalized treatments with minimal off-target consequences. Neoantigen-based cancer immunotherapy has showed promise in cancer treatment due to substantial advancements in omics technology. Bioinformatics, and immunology, which enabled the identification of patient-specific tumor-specific antigens. Using the following keyboards/Phrases: "neoantigen" OR "neo-antigen" OR "tumor-specific antigen" AND "cancer" OR "tumor" OR "oncology" AND "immunotherapy" OR "cancer vaccine" OR "T-cell therapy" AND "prediction" OR "identification" OR "bioinformatics" OR "clinical," a systematic search was conducted across five databases (Google search, Google scholar/ScienceDirect/Scopus/PubMed search engines). The search only included English articles published between 2015 and 2025. Out of 5425 records, 42 full-text articles that met the inclusion criteria were included in the study. Technological breakthrought such as next-generation sequencing (NGS), immunopeptidomics via mass spectrometry, and AI-driven bioinformatics pipelines (pVACtools, NeoPredPipe, Neopepsee) have contributed to neoantigen identification and predictions. These methods help to develop personalized cancer vaccines, adoptive T-cell therapies, and combination therapies that include checkpoint inhibitors. Neoantigen-targeted immunotherapy has great potential for personalized oncology, as it offers a solution to treatment resistant cancer patients. Despite this progress, hurdle remains as only a small proportion of neoantigens truly elicit T cell responses. These models often prioritize common HLA type which limits their global relevance, and , neoantigen prediction algorithms rely solely on genomic and transcriptomic data, thereby neglecting proteomic data, which is essential to confirm that predicted neoantigens are actually translated and presented as peptides. The integration of proteomic tools (spectrometry and immunopeptidomics) into identification workflows remains minimal, and scalability of experimental workflows, as well as the application of these methods across a wide range of cancer types and patients.

Keywords: Cancer Immunotherapy, Neoantigen-based Vaccines, Bioinformatics, Tumor-specific Antigens