肿瘤细胞治疗研究
英文原题：Signal Detection and Machine Learning-Based Prediction of Cytokine Release Syndrome in B-Cell Maturation Antigen-Targeting Immunotherapies Using FAERS Data.
结果：在最终数据集纳入的 4046 份报告中，CAR-T 疗法显示出比 BsAbs 更高的 CRS 报告比值比（aOR：2.55，95% CI：2.16-3.01）。
背景/目的：包括CAR-T 细胞疗法和双特异性抗体（BsAb）在内的B细胞成熟抗原（BCMA）靶向免疫疗法改善了多发性骨髓瘤的临床结局。然而，细胞因子释放综合征（CRS）仍是主要安全性问题，且不同BCMA靶向药物之间的真实世界比较证据有限。本研究利用FDA不良事件报告系统（FAERS）数据评估并比较BCMA靶向CAR-T和BsAb相关CRS报告模式，并通过机器学习方法识别CRS报告的预测因素。 方法：采用2021年第1季度至2025年第3季度的FAERS报告开展药物警戒分析。使用报告比值比（ROR）、比例报告比（PRR）和信息成分（IC）进行不均衡分析，满足预设阈值时判定存在信号。采用多变量Logistic回归，在校正人口学和报告特征后估计CRS报告的校正优势比（aOR）。建立包括XGBoost、LightGBM和随机森林在内的机器学习模型，以预测CRS报告；使用SHAP解释模型。 结果：最终数据集共纳入4,046份报告。与BsAb相比，CAR-T疗法的CRS报告优势较高（aOR=2.55，95% CI 2.16–3.01）。各项不均衡分析指标均发现CAR-T疗法存在显著CRS信号，而BsAb未达到信号检出阈值。在具体药物层面，idecabtagene vicleucel是唯一满足全部预设信号检出标准的药物，且多变量分析显示其报告信号最强（aOR=6.96，95% CI 5.53–8.75）。在评估的模型中，LightGBM的测试集AUROC最高（0.762）。SHAP分析确定idecabtagene vicleucel、美国地区和报告年份是CRS报告最重要的预测因素。 结论：与BsAb相比，CAR-T疗法，尤其是idecabtagene vicleucel，具有更高的CRS报告优势；不同BCMA靶向免疫疗法之间存在显著药物层面异质性。整合药物警戒和机器学习方法，或可识别BCMA靶向疗法中药物特异性的CRS风险差异，从而促进更个体化的安全性监测。
Background/Objectives : B-cell maturation antigen (BCMA)-directed immunotherapies, including chimeric antigen receptor T-cell (CAR-T) therapies and bispecific antibodies (BsAbs), have improved clinical outcomes in multiple myeloma. However, cytokine release syndrome (CRS) remains a major safety concern, and comparative real-world evidence across BCMA-directed agents remains limited. This study aimed to evaluate and compare CRS reporting patterns associated with BCMA-targeted CAR-T and BsAb therapies using the FDA Adverse Event Reporting System (FAERS) data and to identify predictors of CRS reporting using machine learning-based approaches. Methods : A pharmacovigilance analysis was conducted using FAERS reports from 2021 Q1 to 2025 Q3. Disproportionality analyses were performed using the reporting odds ratio (ROR), proportional reporting ratio (PRR), and information component (IC), and signals were considered present when predefined thresholds were met. Multivariable logistic regression was applied to estimate adjusted odds ratios (aORs) for CRS reporting while adjusting for demographic and reporting characteristics. Machine learning models, including XGBoost, LightGBM, and random forest were developed to predict CRS reporting. Model interpretability was assessed using SHapley Additive exPlanations (SHAP). Results : Among 4046 reports included in the final dataset, CAR-T therapies showed higher CRS reporting odds than BsAbs (aOR: 2.55, 95% CI: 2.16-3.01). Disproportionality analyses identified significant CRS signals for CAR-T therapies across all indices, whereas BsAbs did not meet signal detection thresholds. At the agent level, idecabtagene vicleucel was the only agent meeting all predefined signal detection criteria and exhibited the strongest reporting pattern in multivariable analysis (aOR: 6.96, 95% CI: 5.53-8.75). Among the evaluated models, LightGBM achieved the highest predictive test AUROC (0.762). SHAP analysis identified idecabtagene vicleucel, United States region, and reporting year as the most influential predictors of CRS reporting. Conclusions : CAR-T therapies, particularly idecabtagene vicleucel, exhibited higher CRS reporting odds than BsAbs, with substantial agent-level heterogeneity observed across BCMA-directed immunotherapies. Integrating pharmacovigilance and machine learning approaches may facilitate more individualized safety monitoring by identifying agent-specific differences in CRS risk among BCMA-targeted therapies.
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