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ORIGINAL RESEARCH article
Front. Immunol.
Sec. Cancer Immunity and Immunotherapy
Volume 15 - 2024 |
doi: 10.3389/fimmu.2024.1450173
Identification of Early Predictive Biomarkers for Severe Cytokine Release Syndrome in Pediatric patients with Chimeric antigen receptor (CAR) T-cell Therapy
Provisionally accepted- 1 Department of Hematology/Oncology, National Health Committee Key Laboratory of Pediatric Hematology & Oncology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- 2 bioMérieux (Shanghai) Company Limited, Shanghai, China
- 3 Shanghai Children’s Medical Center–bioMérieux Laboratory, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
- 4 Open Innovation & Partnerships Department, bioMérieux SA, Marcy l’Etoile, France
- 5 Infectious Disease Department, Shanghai Children's Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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.
Keywords: CAR-T cell therapy, Cytokine release syndrome (CRS), biomarker, Early prediction, Decision tree model
Received: 17 Jun 2024; Accepted: 26 Aug 2024.
Copyright: © 2024 Su, Chen, Xie, FLEURIE, Jonquieres, Cao, Li, Liang and Tang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Meng Su, Department of Hematology/Oncology, National Health Committee Key Laboratory of Pediatric Hematology & Oncology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Li Xie, bioMérieux (Shanghai) Company Limited, Shanghai, China
Renaud Jonquieres, Open Innovation & Partnerships Department, bioMérieux SA, Marcy l’Etoile, France
Benshang Li, Department of Hematology/Oncology, National Health Committee Key Laboratory of Pediatric Hematology & Oncology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
Ji Liang, bioMérieux (Shanghai) Company Limited, Shanghai, China
Yanjing Tang, Department of Hematology/Oncology, National Health Committee Key Laboratory of Pediatric Hematology & Oncology, Shanghai Children’s Medical Center, Shanghai Jiao Tong University School of Medicine, Shanghai, China
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