OBJECTIVE

Lung cancer with the highest incidence and mortality in the world. Immune checkpoint inhibitors (ICIs), can bring long-term survival benefits to patients, but also can bring immune-related adverse events (irAEs) in some patients during therapy. Therefore, the aim of this study was to investigate the predictive effect of peripheral blood WBC, NLR, sATP and nATP on irAEs in advanced non-small cell lung cancer (NSCLC).

METHODS

Clinical data of 112 patients with advanced NSCLC who were treated with PD -1/PD -L1 inhibitor in the Fifth Affiliated Hospital of Guangzhou Medical University from December 15, 2019 to April 30, 2023 were retrospectively analyzed. These patients were divided into the irAEs group (n = 27) and non-irAEs group (n = 85). The clinical data of the two groups were compared. Receiver operating characteristic (ROC) curves were drawn to determine the threshold value of baseline peripheral blood parameters to predict the occurrence of irAEs. Multivariate logistic regression analysis was used to explore the relationship between peripheral blood markers and the incidence of irAEs.

RESULTS

The patient characteristics have no significant difference between irAEs and non-irAEs group. But the baseline peripheral blood WBC, sATP and nATP of patients in the irAEs group were higher than those in the non-irAEs group (p < 0.05), and the NLR in irAEs group was similar to in the non-irAEs group (p = 0.639).Univariate analysis showed that high WBC, sATP and nATP may the risk factors for the occurrence of irAEs (p < 0.05). Multivariate logistic regression analysis showed that high sATP and nATP were independent risk factors for the occurrence of irAEs (p < 0.05). The best critical values of WBC, sATP and nATP before treatment for predicting the occurrence of irAEs were 8.165 × 10cells/L (AUC = 0.705) ,484.5 ng/mL (AUC = 0.777), and 156 ng/mL (AUC = 0.840), respectively.

CONCLUSIONS

sATP and nATP were independent risk factors for the occurrence of irAEs in advanced NSCLC patients. This discovery provides a new method to predict the occurrence of irAEs in patients. Based on the prediction results, corresponding treatment measures can be taken to reduce the incidence of adverse events.