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.
CAR-T(CAR-T)细胞在血液系统恶性肿瘤中的应用推动了这种免疫治疗形式的显著进展。
CELL INTELLIGENCE · 肿瘤细胞治疗研究
肿瘤细胞治疗研究
英文原题:Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy.
Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy.
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CAR-T 细胞疗法是一种治疗血液系统恶性肿瘤的革命性新疗法,但它也可能导致显著的不良反应,其中细胞因子释放综合征(CRS)是最常见且可能危及生命的。识别用于预测CRS严重程度的生物标志物对于确保CAR-T 疗法的安全性和有效性至关重要。为实现这一目标,我们对CAR-T 细胞输注前后七种细胞因子、四种常规生化标志物和五种血液学标志物的表达谱进行了表征。
我们的结果显示,IL-2、IFN-、IL-6和IL-10是预测重度CRS(sCRS)的关键细胞因子。值得注意的是,IL-2水平在sCRS早期阶段即升高,并有可能作为最有效的细胞因子用于及时检测该病症的发生。
此外,将这些细胞因子生物标志物与淋巴细胞计数等血液学因素相结合,可进一步增强其预测性能。最后,一个包含淋巴细胞计数、IL-2和IL-6的预测树模型在sCRS早期预测中达到了85.11%的准确率(95% CI = 0.763-0.916)。该模型在一个独立队列中进行了验证,并达到了74.47%的准确率(95% CI = 0.597-0.861)。这一新的预测模型有潜力成为临床实践中评估CRS风险的有效工具。
CAR-T cell therapy is a revolutionary new treatment for hematological malignancies, but it can also result in significant adverse effects, with cytokine release syndrome (CRS) being the most common and potentially life-threatening. The identification of biomarkers to predict the severity of CRS is crucial to ensure the safety and efficacy of CAR-T therapy. To achieve this goal, we characterized the expression profiles of seven cytokines, four conventional biochemical markers, and five hematological markers prior to and following CAR-T cell infusion.
Our results revealed that IL-2, IFN- , IL-6, and IL-10 are the key cytokines for predicting severe CRS (sCRS).
Notably, IL-2 levels rise at an earlier stage of sCRS and have the potential to serve as the most effective cytokine for promptly detecting the condition's onset.
Furthermore, combining these cytokine biomarkers with hematological factors such as lymphocyte counts can further enhance their predictive performance.
Finally, a predictive tree model including lymphocyte counts, IL-2, and IL-6 achieved an accuracy of 85. 11% (95% CI = 0. 763-0. 916) for early prediction of sCRS. The model was validated in an independent cohort and achieved an accuracy of 74. 47% (95% CI = 0. 597-0. 861). This new prediction model has the potential to become an effective tool for assessing the risk of CRS in clinical practice.
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