Cross-cohort analysis identified an immune checkpoint-based signature to predict the clinical outcomes of neuroblastoma

Background Neuroblastoma (NB) places a substantial health burden on families worldwide. This study aimed to develop an immune checkpoint-based signature (ICS) based on the expression of immune checkpoints to better assess patient survival risk and potentially guide patient selection for immunotherapy of NB.Methods Immunohistochemistry integrated with digital pathology was used to determine the expression levels of 9 immune checkpoints in 212 tumor tissues used as the discovery set. The GSE85047 dataset (n=272) was used as a validation set in this study. In the discovery set, the ICS was constructed using a random forest algorithm and confirmed in the validation set to predict overall survival (OS) and event-free survival (EFS). Kaplan-Meier curves with a log-rank test were drawn to compare the survival differences. A receiver operating characteristic (ROC) curve was applied to calculate the area under the curve (AUC).Results Seven immune checkpoints, including PD-L1, B7-H3, IDO1, VISTA, T-cell immunoglobulin and mucin domain containing-3 (TIM-3), inducible costimulatory molecule (ICOS) and costimulatory molecule 40 (OX40), were identified as abnormally expressed in NB in the discovery set. OX40, B7-H3, ICOS and TIM-3 were eventually selected for the ICS model in the discovery set, and 89 patients with high risk had an inferior OS (HR 15.91, 95% CI 8.87 to 28.55, p