Table_1_Identification of the Predictive Models for the Treatment Response of Refractory/Relapsed B-Cell ALL Patients Receiving CAR-T Therapy.docx

Background/AimsChimeric antigen receptor (CAR) T cells for refractory or relapsed (r/r) B-cell acute lymphoblastic leukemia (ALL) patients have shown promising clinical effectiveness. However, the factors impacting the clinical response of CAR-T therapy have not been fully elucidated. We here aimed to identify the independent factors of CAR-T treatment response and construct the models for predicting the complete remission (CR) and minimal residual disease (MRD)-negative CR in r/r B-ALL patients after CAR-T cell infusion.
MethodsUnivariate and multivariate logistic regression analyses were conducted to identify the independent factors of CR and MRD-negative CR. The predictive models for the probability of remission were constructed based on the identified independent factors. Discrimination and calibration of the established models were assessed by receiver operating characteristic (ROC) curves and calibration plots, respectively. The predictive models were further integrated and validated in the internal series. Moreover, the prognostic value of the integration risk model was also confirmed.
ResultsThe predictive model for CR was formulated by the number of white blood cells (WBC), central neural system (CNS) leukemia, TP53 mutation, bone marrow blasts, and CAR-T cell generation while the model for MRD-negative CR was formulated by disease status, bone marrow blasts, and infusion strategy. The ROC curves and calibration plots of the two models displayed great discrimination and calibration ability. Patients and infusions were divided into different risk groups according to the integration model. High-risk groups showed significant lower CR and MRD-negative CR rates in both the training and validation sets (p < 0.01). Furthermore, low-risk patients exhibited improved overall survival (OS) (log-rank p < 0.01), higher 6-month event-free survival (EFS) rate (p < 0.01), and lower relapse rate after the allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CAR-T cell infusion (p = 0.06).
ConclusionsWe have established predictive models for treatment response estimation of CAR-T therapy. Our models also provided new clinical insights for the accurate diagnosis and targeted treatment of r/r B-ALL.

背景/研究目的：针对复发难治性（r/r）B细胞急性淋巴细胞白血病（B-cell acute lymphoblastic leukemia, ALL）患者的嵌合抗原受体（CAR）T细胞疗法已展现出颇具前景的临床疗效，但影响CAR-T治疗临床应答的相关因素尚未完全阐明。本研究旨在明确CAR-T治疗应答的独立影响因素，并构建模型以预测复发难治性B-ALL患者接受CAR-T细胞输注后达到完全缓解（complete remission, CR）及微小残留病（minimal residual disease, MRD）阴性完全缓解的情况。
方法：本研究采用单因素及多因素logistic回归分析，以明确CAR-T治疗后完全缓解与微小残留病阴性完全缓解的独立影响因素；基于筛选得到的独立影响因素构建缓解概率预测模型。分别通过受试者工作特征（receiver operating characteristic, ROC）曲线与校准曲线评估所构建模型的区分度与校准效能，并在内部队列中对预测模型进行整合与验证。此外，本研究还验证了整合风险模型的预后价值。
结果：本研究构建的完全缓解预测模型纳入了白细胞计数（white blood cells, WBC）、中枢神经系统（central neural system, CNS）白血病、TP53突变、骨髓原始细胞比例及CAR-T细胞制备代数；而微小残留病阴性完全缓解预测模型则纳入了疾病状态、骨髓原始细胞比例及输注方案。两款模型的受试者工作特征曲线与校准曲线均展现出优异的区分度与校准效能。研究人员依据整合风险模型将患者划分为不同风险层级，在训练集与验证集中，高危组患者的完全缓解率及微小残留病阴性完全缓解率均显著低于低危组（P<0.01）。此外，低危组患者的总生存期（overall survival, OS）更优（log-rank检验P<0.01），6个月无事件生存期（event-free survival, EFS）率更高（P<0.01），且在CAR-T细胞输注后接受异基因造血干细胞移植（allogeneic hematopoietic stem cell transplantation, allo-HSCT）的患者复发率更低（P=0.06）。
结论：本研究成功构建了可用于评估CAR-T治疗应答的预测模型，上述模型可为复发难治性B-ALL的精准诊断与靶向治疗提供全新的临床思路。