Tumor immunotherapy, enhancing the immune system to kill tumor cells and to exert clinical effects, is a revolutionary breakthrough in the field of tumor therapy. Tumor neoantigens are polypeptides with antigenicity resulting from DNA mutations and the key factors in determining the immunogenicity of tumor cells and the clinical efficacy of tumor immunotherapy. However, neoantigen immunogenicity prediction is one of the most difficult unsolved problems in tumor research.
Recently, Assistant Professor Liu Xuesong’s group of SLST published a research article entitled “Quantification of neoantigen-mediated immunoediting in cancer evolution” in the journal Cancer Research. By analyzing tumor neoantigen-mediated immunoediting signals quantitatively, the authors provided a novel evolutional perspective for judging the immunogenicity of neoantigens, and developed a novel biomarker for predicting the clinical effect of tumor immunotherapy.
In the process of tumor evolution, tumor cells are recognized and cleared by the immune system, which is generally called immunoediting. The Liu’s group has developed a method for quantifying the strength of immunoediting signals based on the distribution of neoantigens, which for the first time realizes the reliable quantification of immunoediting signals in individual tumor samples.
Quantification of neoantigen-mediated immunoediting in cancer evolution
Taking advantages of the newly developed method, Prof. Liu and colleagues provided reliable evidence that negative selection signals from immunoediting are present in human cancers and that there is a negative correlation between immunoediting-elimination signals and immunoediting-escape signals.These findings address the controversial scientific question: whether neoantigen mediated negative selection exists in immunotherapy-naïve cancer patients?
Immunotherapy represented by immune checkpoint inhibitors shows excellent therapeutic effects on some tumor patients. For most tumor patients, however, immunotherapy has no obvious clinical effects. Therefore, how to use markers to find patients with clinical responses to immunotherapy is a key scientific problem in the practice of oncology medicine. This study demonstrates that immunoediting signals in tumor patients can predict the clinical effects of immunotherapy, and the predictive efficacy is better than that of the known biomarkers such as tumor mutation burden.
This study has important theoretical and practical values for in-depth understanding of tumor evolution, tumor neoantigen vaccine design, and tumor immunotherapy biomarker development.
The work was supported by the National Natural Science Foundation of China and the startup fund of ShanghaiTech University. It was also supported by the computing resources of ShanghaiTech’s high-performance computing platform.