Tianjin Medical University Cancer Institute and Hospital, National Clinical Research Center for Cancer, Tianjin, China.
Key Laboratory of Cancer Prevention and Therapy, Tianjin, China.
PeerJ. 2022 Dec 15;10:e14566. doi: 10.7717/peerj.14566. eCollection 2022.
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.
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.
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], = 0.048), MON (HR: 1.74, 95% CI [1.07-2.81], = 0.025), LDH (HR: 0.59, 95% CI [0.36-0.95], = 0.030), and line (HR: 0.57, 95% CI [0.35-0.94], = 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], = 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.
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.
部分肺癌患者可从免疫治疗中获益,但预测免疫治疗反应的生物标志物尚未明确。患者的基线特征可能是最方便和有效的标志物。因此,本研究旨在探讨肺癌患者的基线特征与免疫治疗疗效之间的关系。
回顾性分析 2017 年至 2021 年在天津医科大学肿瘤医院接受免疫治疗的 216 例肺癌患者的临床资料。收集所有基线特征数据,然后进行单因素 log-rank 分析和多因素 COX 回归分析。Kaplan-Meier 分析用于评估患者的无进展生存期(PFS)。基于显著生物标志物构建列线图,以预测接受免疫治疗患者的 PFS 率。我们使用 C 指数和校准曲线评估列线图的预测准确性。
对所有收集的基线因素进行单因素分析显示,年龄、临床分期、白细胞(WBC)、淋巴细胞(LYM)、单核细胞(MON)、嗜酸性粒细胞(AEC)、血红蛋白(HB)、乳酸脱氢酶(LDH)、白蛋白(ALB)和治疗线与免疫治疗后 PFS 显著相关。然后将这 10 个风险因素纳入多因素回归分析,结果表明年龄(HR:1.95,95%CI [1.01-3.78], = 0.048)、MON(HR:1.74,95%CI [1.07-2.81], = 0.025)、LDH(HR:0.59,95%CI [0.36-0.95], = 0.030)和治疗线(HR:0.57,95%CI [0.35-0.94], = 0.026)与接受免疫治疗的肺癌患者的 PFS 显著相关。ALB 较高的患者较 ALB 较低的患者有更大的获益趋势(HR:1.58,95%CI [0.94-2.66], = 0.084)。年龄≥51 岁、ALB 较高、LDH 较低、一线免疫治疗和 MON 较高的患者具有更好的反应率和临床获益。建立了基于年龄、ALB、MON、LDH、治疗线的列线图,以预测接受免疫检查点抑制剂(ICI)治疗的患者的预后。训练队列和验证队列的 C 指数接近,分别为 0.71 和 0.75。校准曲线拟合度高,证实了我们的列线图具有较高的预测价值。
年龄、ALB、MON、LDH、治疗线可作为预测接受免疫治疗的肺癌患者 PFS、反应率和肿瘤控制的可靠预测生物标志物。基于年龄、ALB、MON、LDH、治疗线的列线图对预测晚期肺癌患者免疫治疗 1 年、2 年和 3 年的 PFS 具有重要意义。