Liang Emily C, Huang Jennifer J, Portuguese Andrew J, Ortiz-Maldonado Valentín, Albittar Aya, Wuliji Natalie, Basom Ryan, Jeon Yein, Wu Qian, Torkelson Aiko, Kirchmeier Delaney, Chutnik Abigail, Pender Barbara, Sorror Mohamed, Hill Joshua A, Kopmar Noam E, Banerjee Rahul, Cowan Andrew J, Green Damian, Gopal Ajay K, Poh Christina, Shadman Mazyar, Hirayama Alexandre V, Till Brian G, Kimble Erik L, Iovino Lorenzo, Chapuis Aude G, Otegbeye Folashade, Cassaday Ryan D, Milano Filippo, Turtle Cameron J, Maloney David G, Gauthier Jordan
Clinical Research Division, Fred Hutchinson Cancer Center, Seattle, WA.
Division of Hematology and Oncology, Department of Medicine, University of Washington, Seattle, WA.
Blood Adv. 2025 Feb 11;9(3):606-616. doi: 10.1182/bloodadvances.2024014455.
Immune effector cell-associated hematotoxicity (ICAHT) is associated with morbidity and mortality after chimeric antigen receptor (CAR) T-cell therapy. To date, the factors associated with ICAHT are poorly characterized, and there is no validated predictive model of ICAHT as defined by current consensus criteria. Therefore, we performed comprehensive univariate analyses to identify factors associated with severe (grade 3-4) early ICAHT (eICAHT) in 691 patients who received commercial or investigational CAR T-cell therapy for hematologic malignancies. In univariate logistic regression, preinfusion factors associated with severe eICAHT included disease type (acute lymphoblastic leukemia), prelymphodepletion (pre-LD) blood counts including absolute neutrophil count (ANC), lactate dehydrogenase (LDH), and inflammatory (C-reactive protein [CRP], ferritin, and interleukin-6 [IL-6]) and coagulopathy biomarkers (D-dimer). Postinfusion laboratory markers associated with severe eICAHT included early and peak levels of inflammatory biomarkers (CRP, ferritin, and IL-6), coagulopathy biomarkers (D-dimer), peak cytokine release syndrome grade, and peak neurotoxicity grade. We trained (n = 483) and validated (n = 208) 2 eICAHT prediction models (eIPMs): eIPMPre including preinfusion factors only (disease type and pre-LD ANC, platelet count, LDH, and ferritin) and eIPMPost containing both preinfusion (disease type and pre-LD ANC, platelet count, and LDH) and early postinfusion (day +3 ferritin) factors. Both models generated calibrated predictions and high discrimination (area under the receiver operating characteristic curve in test set, 0.87 for eIPMPre and 0.88 for eIPMPost), with higher net benefit in decision curve analysis for eIPMPost. Individualized predictions of severe eICAHT can be generated from both eIPMs using our online tool (available at https://eipm.fredhutch.org).
免疫效应细胞相关血液毒性（ICAHT）与嵌合抗原受体（CAR）T细胞治疗后的发病率和死亡率相关。迄今为止，与ICAHT相关的因素尚未得到充分表征，并且尚无符合当前共识标准定义的经过验证的ICAHT预测模型。因此，我们对691例接受商业或研究性CAR T细胞治疗血液系统恶性肿瘤的患者进行了全面的单因素分析，以确定与严重（3-4级）早期ICAHT（eICAHT）相关的因素。在单因素逻辑回归中，与严重eICAHT相关的输注前因素包括疾病类型（急性淋巴细胞白血病）、淋巴细胞清除前（pre-LD）血细胞计数，包括绝对中性粒细胞计数（ANC）、乳酸脱氢酶（LDH）以及炎症（C反应蛋白[CRP]、铁蛋白和白细胞介素-6[IL-6]）和凝血功能障碍生物标志物（D-二聚体）。与严重eICAHT相关的输注后实验室指标包括炎症生物标志物（CRP、铁蛋白和IL-6）、凝血功能障碍生物标志物（D-二聚体）的早期和峰值水平、细胞因子释放综合征峰值分级和神经毒性峰值分级。我们训练了（n = 483）并验证了（n = 208）2种eICAHT预测模型（eIPMs）：仅包含输注前因素（疾病类型和pre-LD ANC、血小板计数、LDH和铁蛋白）的eIPMPre以及包含输注前（疾病类型和pre-LD ANC、血小板计数和LDH）和输注后早期（第3天铁蛋白）因素的eIPMPost。两种模型均生成了校准预测和高辨别力（测试集中受试者操作特征曲线下面积，eIPMPre为0.87，eIPMPost为0.88），在决策曲线分析中eIPMPost的净效益更高。使用我们的在线工具（可在https://eipm.fredhutch.org获取），可以从两种eIPMs生成严重eICAHT的个体化预测。
