Identification of early predictive biomarkers for severe cytokine release syndrome in pediatric patients with chimeric antigen receptor T-cell therapy

IF 8.3 2区 材料科学 Q1 MATERIALS SCIENCE, MULTIDISCIPLINARY
Meng Su, Luoquan Chen, Li Xie, Aurore Fleurie, Renaud Jonquieres, Qing Cao, Benshang Li, Ji Liang, Yanjing Tang
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Abstract

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-γ, 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.
识别嵌合抗原受体 T 细胞疗法儿科患者严重细胞因子释放综合征的早期预测性生物标志物
CAR-T 细胞疗法是治疗血液恶性肿瘤的革命性新疗法,但它也可能导致严重的不良反应,其中细胞因子释放综合征(CRS)是最常见的一种,有可能危及生命。确定预测 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。为了实现这一目标,我们对 CAR-T 细胞输注前后的七种细胞因子、四种常规生化标志物和五种血液标志物的表达谱进行了表征。我们的研究结果表明,IL-2、IFN-γ、IL-6 和 IL-10 是预测严重 CRS(sCRS)的关键细胞因子。值得注意的是,IL-2的水平在sCRS的早期阶段就会升高,有可能成为及时发现病情的最有效细胞因子。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,还能进一步提高它们的预测性能。最后,一个包括淋巴细胞计数、IL-2 和 IL-6 的预测树模型在早期预测 sCRS 方面的准确率达到了 85.11%(95% CI = 0.763-0.916)。该模型在一个独立队列中进行了验证,准确率为 74.47%(95% CI = 0.597-0.861)。这一新的预测模型有望成为临床实践中评估 CRS 风险的有效工具。
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来源期刊
ACS Applied Materials & Interfaces
ACS Applied Materials & Interfaces 工程技术-材料科学:综合
CiteScore
16.00
自引率
6.30%
发文量
4978
审稿时长
1.8 months
期刊介绍: ACS Applied Materials & Interfaces is a leading interdisciplinary journal that brings together chemists, engineers, physicists, and biologists to explore the development and utilization of newly-discovered materials and interfacial processes for specific applications. Our journal has experienced remarkable growth since its establishment in 2009, both in terms of the number of articles published and the impact of the research showcased. We are proud to foster a truly global community, with the majority of published articles originating from outside the United States, reflecting the rapid growth of applied research worldwide.
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