DataSheet_1_Elucidation of the Application of Blood Test Biomarkers to Predict Immune-Related Adverse Events in Atezolizumab-Treated NSCLC Patients Using Machine Learning Methods.pdf

BackgroundDevelopment of severe immune-related adverse events (irAEs) is a major predicament to stop treatment with immune checkpoint inhibitors, even though tumor progression is suppressed. However, no effective early phase biomarker has been established to predict irAE until now.
MethodThis study retrospectively used the data of four international, multi-center clinical trials to investigate the application of blood test biomarkers to predict irAEs in atezolizumab-treated advanced non-small cell lung cancer (NSCLC) patients. Seven machine learning methods were exploited to dissect the importance score of 21 blood test biomarkers after 1,000 simulations by the training cohort consisting of 80%, 70%, and 60% of the combined cohort with 1,320 eligible patients.
ResultsXGBoost and LASSO exhibited the best performance in this study with relatively higher consistency between the training and test cohorts. The best area under the curve (AUC) was obtained by a 10-biomarker panel using the XGBoost method for the 8:2 training:test cohort ratio (training cohort AUC = 0.692, test cohort AUC = 0.681). This panel could be further narrowed down to a three-biomarker panel consisting of C-reactive protein (CRP), platelet-to-lymphocyte ratio (PLR), and thyroid-stimulating hormone (TSH) with a small median AUC difference using the XGBoost method [for the 8:2 training:test cohort ratio, training cohort AUC difference = −0.035 (p < 0.0001), and test cohort AUC difference = 0.001 (p=0.965)].
ConclusionBlood test biomarkers currently do not have sufficient predictive power to predict irAE development in atezolizumab-treated advanced NSCLC patients. Nevertheless, biomarkers related to adaptive immunity and liver or thyroid dysfunction warrant further investigation.

背景 严重免疫相关不良事件（immune-related adverse events, irAEs）的发生，是免疫检查点抑制剂治疗过程中被迫中断治疗的核心困境之一，即便此时肿瘤进展已得到抑制。然而截至目前，尚未有有效的早期生物标志物可用于预测irAEs的发生。
方法 本研究回顾性纳入四项国际多中心临床试验的数据，旨在探究血液检测生物标志物在预测接受阿替利珠单抗（atezolizumab）治疗的晚期非小细胞肺癌（non-small cell lung cancer, NSCLC）患者发生irAEs的应用价值。本研究共纳入1320例合格受试者组成合并队列，分别以80%、70%、60%的比例划分为训练集与测试集，通过1000次模拟实验，利用7种机器学习方法解析21种血液检测生物标志物的重要性得分。
结果 本研究中，XGBoost与LASSO模型表现最优，且训练集与测试集的一致性相对更高。当训练集与测试集划分比例为8:2时，基于XGBoost方法构建的10种生物标志物组合模型获得了最优的曲线下面积（area under the curve, AUC），其中训练集AUC为0.692，测试集AUC为0.681。进一步简化后，由C反应蛋白（C-reactive protein, CRP）、血小板与淋巴细胞比值（platelet-to-lymphocyte ratio, PLR）以及促甲状腺激素（thyroid-stimulating hormone, TSH）组成的3种生物标志物组合模型，在XGBoost方法下仅存在微小的中位AUC差值：针对8:2的训练测试集划分比例，训练集AUC差值为-0.035（p < 0.0001），测试集AUC差值为0.001（p=0.965）。
结论 目前，血液检测生物标志物尚不足以准确预测接受阿替利珠单抗治疗的晚期NSCLC患者发生irAEs的风险。不过，与适应性免疫以及肝或甲状腺功能异常相关的生物标志物仍有进一步研究的价值。