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:Classification of patients with relapsed/refractory large B-cell lymphoma who do not develop early CRS/NE toxicity using ZUMA clinical trial data.
Classification of patients with relapsed/refractory large B-cell lymphoma who do not develop early CRS/NE toxicity using ZUMA clinical trial data.
分数与星级只用于站内排序 —— 不代表疗效、安全性或个人适用性。
我们证明,常规临床病理变量可用于识别治疗后早期发生 CRS 和/或 NE 风险低的患者。这些知识可用于帮助治疗中心前瞻性地管理患者护理,包括考虑门诊治疗。
我们旨在开发一个可操作且可行的前瞻性临床模型,以评估毒性风险,帮助嵌合抗原受体(CAR)T细胞治疗提供者管理复发和/或难治性大B细胞淋巴瘤患者。
我们开展了一项观察性、回顾性队列研究,使用来自390例接受CD19 CAR-T 细胞疗法axicabtagene ciloleucel治疗患者的二手数据,这些患者来自两项前瞻性临床试验ZUMA-1和ZUMA-7;这些临床试验在2015年至2019年间入组了复发/难治性大B细胞淋巴瘤患者。我们使用机器学习和统计方法,开发了一个分类模型,用于识别不太可能发生任何级别的早期细胞因子释放综合征(CRS)和神经系统事件(NE)的患者。
我们发现,使用预防性糖皮质激素是治疗后前3天内保持无CRS和无NE的重要因素(p<0.001)。我们通过一组六项淋巴细胞清除前的临床病理特征,确定了一个无早期CRS/NE的最佳模型:既往全身治疗线数、年龄、基线肿瘤负荷(以直径乘积之和衡量)、C反应蛋白、天冬氨酸转氨酶和血红蛋白,该模型在留出验证队列中达到0.71的阳性预测值。此外,我们发现该模型生成的预测概率与2级或更高级别NE的发生率密切相关。
We aimed to develop an actionable and feasible prospective clinical model to estimate toxicity risk to assist chimeric antigen receptor (CAR) T-cell therapy providers with the management of patients with relapsed and/or refractory large B-cell lymphoma.
We conducted an observational, retrospective cohort study using secondary data from 390 patients treated with the CD19 CAR T-cell therapy axicabtagene ciloleucel under two prospective clinical trials, ZUMA-1 and ZUMA-7; these clinical trials enrolled patients with relapsed/refractory large B-cell lymphoma between 2015 and 2019. Using machine learning and statistical methods, we developed a classification model for identifying patients unlikely to experience early cytokine release syndrome (CRS) and neurological events (NE) of any grade.
We found the use of prophylactic corticosteroids to be an important factor in remaining CRS-free and NE-free within the first 3 days post-treatment (p<0.001). We identified a top model for no early CRS/NE using a set of six pre-lymphodepletion clinicopathologic features: number of lines of prior systemic therapy, age, baseline tumor burden (as measured by sum of the product of the diameters), C-reactive protein, aspartate transaminase, and hemoglobin, which achieves a positive predictive value of 0.71 in the holdout validation cohort. Additionally, we find that predicted probabilities generated from the model are strongly associated with incidence of Grade 2 or higher NE.
We illustrated that routine clinicopathologic variables can be used to identify patients at low risk of developing early post-treatment CRS and/or NE. Such knowledge can be used to help treating centers prospectively manage patient care, including consideration of outpatient treatment.
在 PubMed 查看 → 出版商原文(DOI) 全文 PDF(PMC)· 可下载 治疗专题与资料阅读指南 资料来源与翻译说明 报告译文或资料问题 →
MEMBER ACCOUNT
登录成功会直接打开下一页。