Xueyuan Chen,1,* Tingting Liu,1,* Silang Mo,1,* Yuwen Yang,1 Xiang Chen,1 Shaodong Hong,1 Ting Zhou,1 Gang Chen,1 Yaxiong Zhang,1 Yuxiang Ma,2 Yuanzheng Ma,1 Li Zhang,1 Yuanyuan Zhao1 

1Medical Oncology Department, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-Sen University Cancer Center, Guangzhou, 510060, People’s Republic of China; 2Department of Clinical Research, State Key Laboratory of Oncology in South China, Guangdong Provincial Clinical Research Center for Cancer, Sun Yat-sen University Cancer Center, Guangzhou, 510060, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Li Zhang; Yuanyuan Zhao, Sun Yat-sen University Cancer Center, 651 Dong Feng Road East, Guangzhou, 510060, People’s Republic of China, Email zhangli@sysucc.org.cn; zhaoyy@sysucc.org.cn

Purpose: This study aimed to investigate the prognostic value of inflammation markers for advanced pulmonary lymphoepithelioma-like carcinoma (PLELC) and develop an effective prognostic model based on inflammation markers to predict the overall survival (OS) of this population.
Methods: Cox regression analysis was performed on 18 clinical and inflammation features, and a nomogram was created to predict overall survival (OS). The nomogram was evaluated by the concordance index (C-index), the time-dependent area under the receiver operating (ROC) curves (AUCs), calibration curves, and Decision Curve Analysis (DCA).
Results: This study included a training cohort (n = 177) and a validation cohort (n = 77). The following variables were found to be independent prognostic factors for OS and used in the nomogram: Hepatitis B virus surface antigen status, gender, neutrophil-to-lymphocyte ratio (NLR), and C-reactive protein-to-albumin ratio (CAR). The C-indexes of the nomogram in the training and validation cohort were 0.740 (95% CI: 0.706– 0.747) and 0.733 (95% CI: 0.678– 0.788), respectively. Furthermore, time-dependent AUCs and well-fitted calibration curves showed good discriminative ability in both cohorts. Additionally, among the subset of EBV DNA data (n = 111), both ROC curve and DCA curve analysis demonstrated that the nomogram plus EBV DNA provided superior predictive performance compared to EBV DNA or the nomogram alone. Patients who received chemoimmunotherapy as the first-line treatment had better OS (not reached vs 44.4 months, P = 0.015) than those with chemotherapy alone and those who received immunotherapy at any line had better OS than those who never received it (not reached vs 31.0 months, P < 0.001).
Conclusion: This study established and validated a prognostic nomogram model for patients with advanced PLELC. Combining the nomogram with EBV DNA more effectively predicted the prognosis of patients than the nomogram alone. Immunotherapy was found to be a critical treatment option for PLELC.

Keywords: pulmonary lymphoepithelioma-like carcinoma, overall survival, inflammation markers, nomogram