A machine learning-based method to optimize the immunogenicity of human leukocyte antigen class I-restricted neoantigens.

Neoantigens, arising from somatic mutations, have the potential to induce immune responses against tumor cells. As natural neoantigens that drive immune responses are uncommon, some methods to optimize neoantigens have been devised, leading to "heteroclitic" neoantigens capable of triggering cross-immunization. Existing methods, however, concentrate exclusively on the affinity of neoantigens for human leukocyte antigen (HLA), while neglecting the enhancement of their immunogenicity. Here, we developed a machine learning-based method, Naso, which integrates simulated annealing search and multi-objective training to optimize the immunogenicity of neoantigens. We also designed a search space optimization strategy to improve search efficiency. Experimental evaluation demonstrated that Naso outperforms existing methods in enhancing the immunogenicity of neoantigens. Specifically, Naso can transform nonimmunoreactive neoantigens into immunoreactive ones with no more than three mutation amino acids. Moreover, Naso can obtain competitive results in other immunological features, such as binding affinity and presentation. Consequently, Naso-optimized neoantigens can elicit an immune response and facilitate cross-immunization, thereby promoting the development of heteroclitic neoantigen-based vaccines. The source code of Naso is available at https://github.com/lyotvincent/Naso.