CAR-T(CAR-T)细胞疗法在非肿瘤性疾病中的应用
Chimeric antigen receptor T (CAR-T) cell therapy in non-oncological diseases.
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Predictive model for CAR-T cell therapy success in patients with relapsed/refractory B-cell acute lymphoblastic leukaemia.
Predictive model for CAR-T cell therapy success in patients with relapsed/refractory B-cell acute lymphoblastic leukaemia.
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CAR-T 治疗复发/难治性B细胞急性淋巴细胞白血病疗效显著,但部分患者无法获益。本研究纳入55例患者和22名健康供者,通过流式分析外周血淋巴细胞亚群,评估CAR-T 制备及治疗结局的预测指标。B细胞、调节性T细胞(Treg)和外周血微小残留白血病细胞可预测CAR-T 制备成功,曲线下面积分别为0.936、0.857和0.914。基于CD3阳性T细胞计数、CD4/CD8比值、Treg及髓外病变的模型可预测治疗反应。另一模型纳入CD4/CD8比值、B细胞、Treg和髓外病变,预测CAR-T 治疗成功的AUC为0.966,特异度92.59%、敏感度91.67%;在验证组中敏感度为100%、特异度为90.91%。研究识别出多个预测指标并建立具有稳健预测能力的模型,有望辅助CAR-T 临床决策。
Chimeric 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.
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