Dynamic serum biomarkers to predict the efficacy of PD-1 in patients with nasopharyngeal carcinoma

Cancer Cell International

Rongzeng Cai¹  Hao Chen¹  Ao Zhang¹  Luocan Wang¹  Runkun Han¹  Guanqing Zhong¹  Shulin Chen¹  Caixia Xu²  Peng Sun³ 

[1] Department of Clinical Laboratory, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, 510060, Guangzhou, People’s Republic of China;Research Center for Translational Medicine, the First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, 510080, Guangzhou, Guangdong, P.R. China;Research Center for Translational Medicine, the First Affiliated Hospital, Sun Yat-Sen University, 58 Zhongshan Road 2, 510080, Guangzhou, Guangdong, P.R. China;Department of Medical Oncology, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Sun Yat-Sen University Cancer Center, 510060, Guangzhou, People’s Republic of China;

关键词: Biomarker;    PD-1;    Nasopharyngeal carcinoma;    ICB;    Dynamic monitor;   

DOI  :  10.1186/s12935-021-02217-y

来源: Springer

【 摘 要 】

BackgroundThere is a lack of effective treatments for recurrent or metastatic nasopharyngeal carcinoma (RM-NPC). Furthermore, the response rate of NPC patients to programmed death 1 (PD-1) inhibitors is approximately 20% to 30%. Thus, we aimed to explore reliable and minimally invasive prognostic indicators to predict the efficacy of PD-1 inhibitors combination therapy in RM-NPC.MethodsThe serum markers of 160 RM-NPC patients were measured before and three weeks after the first anti-PD-1 treatment. The least absolute shrinkage and selection operator (LASSO) logistic regression was carried out to select dynamic serum indicators and construct a prediction model. Furthermore, we carried out univariate, multivariate, nomogram and survival analyses to identify independent prognostic factors that were associated with 1-year progression-free survival (PFS).ResultsBased on two markers that were screened by Lasso logistic regression, we constructed a risk score prediction model for the prediction of anti-PD-1 efficacy at 8–12 weeks with an AUC of 0.737 in the training cohort and 0.723 in the validation cohort. Risk score and metastases were included in the nomogram, and the Kaplan–Meier survival curves demonstrated that the high-risk group has shorter PFS compared to the low-risk group. The concordance index (C-index) of the nomogram for PFS is higher than that of the TNM stage in the training and validation cohort.ConclusionWe proposed a strategy to monitor dynamic changes in the biochemistry markers and emphasized their importance as potential prognostic biomarkers for the treatment of advanced NPC treated with PD-1 inhibitors. Our risk score prediction model was based on the dynamic change of LDH and AST/ALT, which has predictive and prognostic value for NPC patients who were treated with PD-1 inhibitors.

【 授权许可】

CC BY