Rapid and direct discovery of functional tumor specific neoantigens by high resolution mass spectrometry and novel algorithm prediction.

While immune cell therapies have transformed cancer treatment, achieving comparable success in solid tumors remains a significant challenge compared to hematologic malignancies like non-Hodgkin lymphoma (NHL) and multiple myeloma (MM). Over the past four decades, various immunotherapeutic strategies, including tumor vaccines, tumor-infiltrating lymphocyte (TIL) therapies, and T cell receptor (TCR) therapies, have demonstrated clinical efficacy in select solid tumors, suggesting potential advantages over CAR-T and CAR-NK cell therapies in specific contexts. The dynamic nature of the cancer-immunity cycle, characterized by the continuous evolution of tumor-specific neoantigens, enables tumors to evade immune surveillance. This highlights the urgent need for rapid and accurate identification of functional tumor neoantigens to inform the design of personalized tumor vaccines. These vaccines can be based on mRNA, dendritic cells (DCs), or synthetic peptides. In this study, we established a novel platform integrating immunoprecipitation-mass spectrometry (IP-MS) for efficient and direct identification of tumor-specific neoantigen peptides. By combining this approach with our proprietary AI-based prediction algorithm and high-throughput functional validation, we can generate patient-specific neoantigen candidates within six weeks, accelerating personalized tumor vaccine development.