Xu Qifan, Li Yang, Xin Beibei, Wang Chenyang, Tao Xinya, Li Shihai, Xiong Hui, Zhou Xiaohua, Wang Li, Zhao Weili
Shanghai Institute of Hematology, State Key Laboratory of Medical Genomics, National Research Center for Translational Medicine at Shanghai, Ruijin Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, China.
Beijing International Center for Mathematical Research, Department of Biostatistics, Peking University, Beijing, China.
Ann Hematol. 2025 Sep 10. doi: 10.1007/s00277-025-06525-5.
Approximately 30-40% of diffuse large B-cell lymphoma (DLBCL) patients will develop relapse/refractory disease, who may benefit from novel therapies, such as CAR-T cell therapy. Thus, accurate identification of individuals at high risk of early chemoimmunotherapy failure (ECF) is crucial. Methods. Two prognostic models were developed to predict the ECF of DLBCL using clinical variables, namely the ECF-IPI-basic model (n = 1200) and the ECF-IPI-advance model (n = 699), respectively. 8 variables included age, gender, Ann Arbor stage, Hans classification, MYC and BCL2 double expression (DE), number of extranodal involvement sites, lactate dehydrogenase (LDH) and Eastern Cooperative Oncology Group performance status (ECOG PS) were considered to construct the basic model. The advanced model incorporated four additional biomarkers, interleukin-8 (IL-8), interleukin-2 receptor (IL-2R), β2-microglobulin (β2-MG), and D-dimer, totaling 12 predictive variables. Results. The ECF-IPI-basic model includes 5 variables, which was constructed with the formula of Age + Ann Arbor stage + DE (MYC and BCL2 double expression) + ECOG + LDH (lactate dehydrogenase). The ECF-IPI-advance model includes 7 variables, specifically, it was constructed with the formula of Age × Sex + Ann Arbor stage + DE + ECOG + LDH + IL-2R. Compared with the IPI score, greater discriminatory capacity was observed in both of the ECF-IPI-basic model (AUC, 0.768 vs. 0.701, p < 0.001) and the ECF-IPI-advance model (AUC, 0.824 vs. 0.724, p < 0.001) in identifying ECF. Conclusions. Overall, this study provides two potent ECF-IPI models that can effectively distinguish the patients with ECF from DLBCL, contributing to improve the prognosis of DLBCL.
大约30%-40%的弥漫性大B细胞淋巴瘤(DLBCL)患者会出现复发/难治性疾病,这些患者可能从新型疗法中获益,如嵌合抗原受体T细胞(CAR-T)疗法。因此,准确识别早期化疗免疫治疗失败(ECF)高风险个体至关重要。方法。利用临床变量分别构建了两个预测DLBCL患者ECF的预后模型,即ECF-IPI基础模型(n = 1200)和ECF-IPI进阶模型(n = 699)。构建基础模型时考虑了8个变量,包括年龄、性别、Ann Arbor分期、Hans分类、MYC和BCL2双表达(DE)、结外受累部位数量、乳酸脱氢酶(LDH)和东部肿瘤协作组体能状态(ECOG PS)。进阶模型纳入了另外4个生物标志物,即白细胞介素-8(IL-8)、白细胞介素-2受体(IL-2R)、β2-微球蛋白(β2-MG)和D-二聚体,共有12个预测变量。结果。ECF-IPI基础模型包括5个变量,其构建公式为年龄+Ann Arbor分期+DE(MYC和BCL2双表达)+ECOG+LDH(乳酸脱氢酶)。ECF-IPI进阶模型包括7个变量,具体构建公式为年龄×性别+Ann Arbor分期+DE+ECOG+LDH+IL-2R。与国际预后指数(IPI)评分相比,ECF-IPI基础模型(AUC,0.768对0.701,p < 0.001)和ECF-IPI进阶模型(AUC,0.824对0.724,p < 0.001)在识别ECF方面均表现出更强的区分能力。结论。总体而言,本研究提供了两个有效的ECF-IPI模型,能够有效区分DLBCL患者中的ECF患者,有助于改善DLBCL患者的预后。