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A Syngeneic Mouse B-Cell Lymphoma Model for Pre-Clinical Evaluation of CD19 CAR T Cells
Published on: October 16, 2018
Prognostic factor analysis of CD19 CAR-T therapy followed by hematopoietic stem cell transplantation in
Xin Wang1, Linlin Liu1, Yuanyuan Zhai1
1First Central Hospital of Tianjin Medical University, Tianjin Medical University, Tianjin, China.
Abstract:
To evaluate the long-term prognosis of patients with relapsed/refractory B-cell acute lymphoblastic leukemia (R/R B-ALL) who received sequential allogeneic hematopoietic stem cell transplantation (allo-HSCT) following CD19 Chimeric Antigen Receptor T-cell (CAR-T) therapy, and to identify the risk factors influencing overall survival (OS) and the severity of acute graft-versus-host disease (aGVHD). A retrospective analysis was conducted on 63 patients with R/R B-ALL who received sequential allo-HSCT after CD19 CAR-T therapy. Cox regression analysis was used to identify factors influencing OS. Patients were stratified according to the severity of aGVHD (low-grade vs. high-grade). Logistic regression was employed to identify predictors of severe aGVHD, and a nomogram was constructed based on these predictors. The model was internally validated using receiver operating characteristic (ROC) curves, calibration plots, and decision curve analysis (DCA). The two-year OS and progression-free survival (PFS) rates were 67.7% and 55.6%, respectively.The time interval from CAR-T therapy to transplantation, along with high-grade aGVHD, were identified as independent risk factors for OS, whereas low-grade aGVHD exerted a protective effect. A longer time interval from CAR-T therapy to transplantation and pre-transplant minimalresidual disease (MRD)-positive status were independent predictors of severe aGVHD. The nomogram developed for predicting the severity of aGVHD demonstrated good discrimination (areaunder the curve [AUC] = 0.827), satisfactory calibration, and clinical utility upon internal validation. We identified key prognostic factors and developed a validated nomogram that enables early, individualized prediction of severe aGVHD in this setting. This model can assist in early risk stratification and targeted intervention to improve outcomes.
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