Intelligent Systems Science and Engineering College, Harbin Engineering University, Harbin, China.
Shanghai Unicar-Therapy BioMedicine Technology Co., Ltd, Shanghai, China.
Front Immunol. 2023 Oct 3;14:1273507. doi: 10.3389/fimmu.2023.1273507. eCollection 2023.
CAR-T cell therapy is a novel approach in the treatment of hematological tumors. However, it is associated with life-threatening side effects, such as the severe cytokine release syndrome (sCRS). Therefore, predicting the occurrence and development of sCRS is of great significance for clinical CAR-T therapy. The study of existing clinical data by artificial intelligence may bring useful information.
By analyzing the heat map of clinical factors and comparing them between severe and non-severe CRS, we can identify significant differences among these factors and understand their interrelationships. Ultimately, a decision tree approach was employed to predict the timing of severe CRS in both children and adults, considering variables such as the same day, the day before, and initial values.
We measured cytokines and clinical biomarkers in 202 patients who received CAR-T therapy. Peak levels of 25 clinical factors, including IFN-γ, IL6, IL10, ferritin, and D-dimer, were highly associated with severe CRS after CAR T cell infusion. Using the decision tree model, we were able to accurately predict which patients would develop severe CRS consisting of three clinical factors, classified as same-day, day-ahead, and initial value prediction. Changes in serum biomarkers, including C-reactive protein and ferritin, were associated with CRS, but did not alone predict the development of severe CRS.
Our research will provide significant information for the timely prevention and treatment of sCRS, during CAR-T immunotherapy for tumors, which is essential to reduce the mortality rate of patients.
嵌合抗原受体 T 细胞（CAR-T）疗法是治疗血液系统肿瘤的一种新方法。然而，它与危及生命的副作用相关，如严重细胞因子释放综合征（sCRS）。因此，预测 sCRS 的发生和发展对于临床 CAR-T 治疗具有重要意义。通过人工智能对现有临床数据的研究可能会带来有用的信息。
通过分析临床因素的热图，并比较严重和非严重 CRS 之间的差异，我们可以识别出这些因素之间的显著差异，并了解它们之间的相互关系。最终，采用决策树方法来预测儿童和成人严重 CRS 的发生时间，考虑了当天、前一天和初始值等变量。
我们对接受 CAR-T 治疗的 202 名患者测量了细胞因子和临床生物标志物。在 CAR-T 细胞输注后，25 种临床因素的峰值水平，包括 IFN-γ、IL6、IL10、铁蛋白和 D-二聚体，与严重 CRS 高度相关。使用决策树模型，我们能够准确预测哪些患者会出现严重 CRS，包括三个临床因素，分别为当天、前一天和初始值预测。血清生物标志物（如 C 反应蛋白和铁蛋白）的变化与 CRS 相关，但不能单独预测严重 CRS 的发生。
我们的研究将为肿瘤患者接受 CAR-T 免疫治疗期间 sCRS 的及时预防和治疗提供重要信息，这对于降低患者死亡率至关重要。
