Establishment and Evaluation of Immune Checkpoint Inhibitor Treatment Response Model Based on Gene Expression Value

Background: In the past 10 years, the identification of new mutant genes involved in the pathogenesis of melanoma and the discovery of key immune checkpoints have promoted the development of targeted therapy and immunotherapy. There is no doubt that an important breakthrough has been made in the treatment of advanced or metastatic melanoma. However, the treatment of melanoma also faces many challenges. In addition to resistance to existing targeted therapies or immunotherapy, most patients do not respond to immunotherapy or have serious adverse reactions. At present, the value of existing biomarkers to predict treatment response and toxicity is still limited. Therefore, there is an urgent need to establish a convenient and reliable immunotherapy response prediction model in order to preliminarily clarify the population benefiting from immunotherapy. Results: : We established a predictive model based on the expression values of five genes for patients with melanoma with an anti-PD1 immunotherapy response score. This model showed better predictive ability compared with other common immunotherapy predictors. Differences were found in the number of immune cells and the expression of common immune checkpoint genes between the high- and low-score groups. The model played a pivotal role in predicting renal cell carcinoma anti-PD1 immunotherapy response. Conclusions: : The anti-PD1 immunotherapy response score prediction model for patients with melanoma showed good predictive power, thus having far-reaching significance for identifying people who benefited from anti-PD1 immunotherapy and reducing the potential toxicity of insensitive patients.