预测弥漫大 B 细胞淋巴瘤早期化疗免疫治疗失败的国际预后指数

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), 2-microglobulin ( 2-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.