Predictive model for CAR‐T cell therapy success in patients with relapsed/refractory B‐cell acute lymphoblastic leukaemia

• DOI: 10.1111/sji.13352
• Publication Date: 2024-01-17T05:53:25Z
• Authors: Lianfang Pu; Huiping Wang; Fan Wu; Furun An; Hao Xiao; Yangyang Wang; Xue Liang; Zhimin Zhai
• Keywords: Male; Adult; Receptors, Chimeric Antigen; Adolescent; Middle Aged; Immunotherapy, Adoptive; T-Lymphocytes, Regulatory; Young Adult; 03 medical and health sciences; Treatment Outcome; 0302 clinical medicine; ROC Curve; Recurrence; Precursor B-Cell Lymphoblastic Leukemia-Lymphoma; Child, Preschool; Humans; Female; Child

AbstractChimeric antigen receptor T‐cell (CAR‐T) therapy has demonstrated remarkable efficacy in treating relapsed/refractory acute B‐cell lymphoblastic leukaemia (R/R B‐ALL). However, a subset of patients does not benefit from CAR‐T therapy. Our study aims to identify predictive indicators and establish a model to evaluate the feasibility of CAR‐T therapy. Fifty‐five R/R B‐ALL patients and 22 healthy donors were enrolled. Peripheral blood lymphocyte subsets were analysed using flow cytometry. Sensitivity, specificity, accuracy, positive and negative predictive values and receiver operating characteristic (ROC) areas under the curve (AUC) were determined to evaluate the predictive values of the indicators. We identified B lymphocyte, regulatory T cell (Treg) and peripheral blood minimal residual leukaemia cells (B‐MRD) as indicators for predicting the success of CAR‐T cell preparation with AUC 0.936, 0.857 and 0.914. Furthermore, a model based on CD3+ T count, CD4+ T/CD8+ T ratio, Treg and extramedullary diseases (EMD) was used to predict the response to CAR‐T therapy with AUC of 0.938. Notably, a model based on CD4+ T/CD8+ T ratio, B, Treg and EMD were used in predicting the success of CAR‐T therapy with AUC 0.966 [0.908–1.000], with specificity (92.59%) and sensitivity (91.67%). In the validated group, the predictive model predicted the success of CAR‐T therapy with specificity (90.91%) and sensitivity (100%). We have identified several predictive indicators for CAR‐T cell therapy success and a model has demonstrated robust predictive capacity for the success of CAR‐T therapy. These results show great potential for guiding informed clinical decisions in the field of CAR‐T cell therapy.