Early prediction of radiation pneumonitis in patients with lung cancer treated with immunotherapy through monitoring of plasma chemokines.

Abstract

Objective: This study is aimed to identify biomarkers for symptomatic radiation pneumonitis (RP) in patients with lung cancer treated with immune checkpoint inhibitors (ICIs).

Methods: This multicenter, prospective study enrolled lung cancer patients receiving thoracic radiotherapy (RT) between 2021 and 2023. Plasma cytokines were measured using Luminex assays. Cox proportional hazards model was used to identify risk factors and biomarkers for RP. Sensitivity analysis was conducted using Fine-Gray competing risk analyses. Receiver operating characteristic (ROC) curves were used to assess the predictive value of the cytokines.

Results: A total of 214 patients receiving thoracic RT were included in this study, with 75 (31.12%) patients developing symptomatic RP. Among the 71 patients with prior ICI treatment, 32 (45.07%) developed symptomatic RP. Patients with prior ICI treatment had higher incidence of symptomatic RP and plasma chemokines than those without prior ICI treatment. For patients with prior ICI treatment, plasma CXCL10 before RT (hazard ratio[HR]=1.29, 95% confidence interval[CI]: 1.03-1.64) and at 2 weeks (HR=1.28, 95%CI: 1.03-1.59) and 4 weeks during RT (HR=1.65, 95%CI: 1.19-2.28) were significant associated with RP. The area under the curves (AUC) of plasma CXCL10 at baseline, 2w and 4w during RT were 0.625, 0.680, and 0.679, respectively. Plasma CXCL14 before RT and CXCL2 during RT were also predictors of RP. A risk score integrating CXCL10, CXCL14, CXCL2, and mean lung dose (MLD) showed better predictive performance than individual factors (AUC=0.757).

Conclusions: In this prospective study, plasma chemokines predict future risk of symptomatic RP in patients with lung cancer who have received prior immunotherapy. Despite with moderate AUC, the scoring system based on plasma chemokines and MLD is a feasible tool for predicting symptomatic RP, aiding in tailoring personalized and optimal treatment for patients.