Identification of prognostic factors and nomogram model for patients with advanced lung cancer receiving immune checkpoint inhibitors

PeerJ

article

Xiuqiong Chen¹  Zhaona Li¹  Jing Zhou¹  Qianhui Wei¹  Xinyue Wang¹  Richeng Jiang¹ 

[1] Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer;Key Laboratory of Cancer Prevention and Therapy;Tianjin’s Clinical Research Center for Cancer;Department of Thoracic Oncology, Tianjin Lung Cancer Center, Tianjin Cancer Institute and Hospital, Tianjin Medical University

关键词: Lung cancer;    Immunotherapy;    Biomarkers;    Nomogram;    Progression-free survival;   

DOI  :  10.7717/peerj.14566

学科分类：社会科学、人文和艺术（综合）

来源: Inra

【 摘 要 】

Background and aim Some patients with lung cancer can benefit from immunotherapy, but the biomarkers that predict immunotherapy response were not well defined. Baseline characteristic of patients may be the most convenient and effective markers. Therefore, our study was designed to explore the association between baseline characteristics of patients with lung cancer and the efficacy of immunotherapy. Methods A total of 216 lung cancer patients from Tianjin Medical University Cancer Institute & Hospital who received immunotherapy between 2017 and 2021 were included in the retrospective analysis. All baseline characteristic data were collected and then univariate log-rank analysis and multivariate COX regression analysis were performed. Kaplan–Meier analysis was used to evaluate patients’ progression-free survival (PFS). A nomogram based on significant biomarkers was constructed to predict PFS rate of patients receiving immunotherapy. We evaluated the prediction accuracy of nomogram using C-indices and calibration curves. Results Univariate analysis of all collected baseline factors showed that age, clinical stage, white blood cell (WBC), lymphocyte (LYM), monocyte (MON), eosinophils (AEC), hemoglobin (HB), lactate dehydrogenase (LDH), albumin (ALB) and treatment line were significantly associated with PFS after immunotherapy. Then these 10 risk factors were included in a multivariate regression analysis, which indicated that age (HR: 1.95, 95% CI [1.01–3.78], P = 0.048), MON (HR: 1.74, 95% CI [1.07–2.81], P = 0.025), LDH (HR: 0.59, 95% CI [0.36–0.95], P = 0.030), and line (HR: 0.57, 95% CI [0.35–0.94], P = 0.026) were significantly associated with PFS in patients with lung cancer receiving immunotherapy. Patients with higher ALB showed a greater trend of benefit compared with patients with lower ALB (HR: 1.58, 95% CI [0.94–2.66], P = 0.084). Patients aged ≥51 years, with high ALB, low LDH, first-line immunotherapy, and high MON had better response rates and clinical benefits. The nomogram based on age, ALB, MON, LDH, line was established to predict the prognosis of patients treated with immune checkpoint inhibitor (ICI). The C-index of training cohort and validation cohort were close, 0.71 and 0.75, respectively. The fitting degree of calibration curve was high, which confirmed the high prediction value of our nomogram. Conclusion Age, ALB, MON, LDH, line can be used as reliable predictive biomarkers for PFS, response rate and cancer control in patients with lung cancer receiving immunotherapy. The nomogram based on age, ALB, MON, LDH, line was of great significance for predicting 1-year-PFS, 2-year-PFS and 3-year-PFS in patients with advanced lung cancer treated with immunotherapy.

【 授权许可】

CC BY