DataSheet_3_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：尽管免疫检查点抑制剂可抑制肿瘤进展，但严重免疫相关不良事件（immune-related adverse events, irAEs）的发生是导致免疫检查点抑制剂治疗被迫中断的主要困境。然而截至目前，尚未有有效的早期生物标志物可用于预测irAE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，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irAE的应用价值。本研究共纳入1320例合格患者组成合并队列，分别以80%、70%、60%的比例将队列划分为训练集与对应测试集，经1000次模拟后，采用7种机器学习方法解析21项血液检测生物标志物的重要性得分。
结果：本研究中，XGBoost与LASSO表现最优，训练集与测试集的一致性相对更高。当训练集与测试集比例为8:2时，采用XGBoost方法构建的10种生物标志物组合获得了最佳曲线下面积（area under the curve, AUC）：训练集AUC为0.692，测试集AUC为0.681。进一步可将该组合缩减为包含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）的3种生物标志物组合，采用XGBoost方法时，其曲线下面积的中位数差异极小[训练集与测试集比例为8:2时，训练集AUC差异为−0.035（p < 0.0001），测试集AUC差异为0.001（p=0.965）]。
结论：目前，血液检测生物标志物对于预测接受阿替利珠单抗治疗的晚期NSCLC患者发生irAE的预测效能仍显不足。不过，与适应性免疫、肝或甲状腺功能异常相关的生物标志物仍值得进一步探索。