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:Validation of Cure Assumptions when Analyzing ZUMA-7 Follow-up Data of Axicabtagene Ciloleucel and Standard of Care Therapy in Second-Line Relapsed/Refractory Large B-cell Lymphoma.
Validation of Cure Assumptions when Analyzing ZUMA-7 Follow-up Data of Axicabtagene Ciloleucel and Standard of Care Therapy in Second-Line Relapsed/Refractory Large B-cell Lymphoma.
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重复既往研究结果,与标准参数建模相比,MCM 方法能为接受 CAR-T 细胞治疗的 R/R DLBCL 患者提供更准确的长期 OS 估计。
ZUMA-7是规模最大的嵌合抗原受体(CAR)T细胞疗法随机对照试验(RCT),比较阿基仑赛(axi-cel)与历史标准治疗(HSoC)在复发/难治性弥漫大B细胞淋巴瘤(R/R DLBCL)中的疗效。ZUMA-7两组均包括可能治愈疾病的治疗,但治疗完成率和获得根治性治疗的时间不同,导致治愈比例和治愈时间不同。混合治愈模型(MCM)被认为更适合设计和估算根治性疗法的临床研究效能,也可在模拟研究中外推长期结局。本研究旨在通过分析其他成本效果研究中的外推结果,评估MCM用于DLBCL二线(2L)治疗是否恰当;利用ZUMA-7试验不同数据截点验证MCM,并描述治疗特异性治愈时间和治愈比例可能造成的差异。
开展定向文献综述(TLR),识别使用ZUMA-7临床试验数据对axi-cel长期生存进行外推的成本效果模型研究。分析使用ZUMA-7中axi-cel及历史HSoC两组的总生存期(OS)患者个体数据,数据分别来自主要无事件生存期(EFS)截点(中位随访24.9个月)和主要OS截点(中位随访47.2个月)。模拟3组中间数据集,比较不同数据截点下治愈比例及外推预测准确性,并评估试验组间差异。
TLR显示,与外部研究的外推相比,使用ZUMA-7 OS数据截点进行的外推与长期观察数据之间的差异较小。在所测试模型中,MCM拟合最佳;axi-cel组外推的百分比误差为2%–27%。根据主要EFS截点的观察数据,两组治愈比例范围分别为axi-cel 24%–54%、HSoC 35%–49%。基于更成熟的OS观察数据进行外推后,治愈比例范围缩窄:axi-cel为50%–54%,HSoC为41%–50%。
与标准参数模型相比,对于接受CAR-T 细胞治疗的R/R DLBCL患者,MCM方法能更准确估算长期OS,与既往研究结果一致。但这些发现也凸显了开展适当临床基准对照的重要性,以确保用于成本效果分析和决策的长期外推结果在临床上合理且表面效度可信。
ZUMA-7 is the largest randomized controlled trial (RCT) for chimeric antigen receptor (CAR) T-cell therapy, which compared axicabtagene ciloleucel (axi-cel) to historical standard of care (HSoC) for the treatment of relapsed or refractory diffuse large B-cell lymphoma (R/R DLBCL). Both arms of ZUMA-7 contained potentially curative treatments; however, differences in the treatment completion rate and timing to receive definitive treatment led to differences in the extent and timing of cure. Mixture cure modeling (MCM) has been suggested as a superior method in designing and powering clinical studies of curative therapies but also for extrapolation of long-term outcomes in simulation studies. The aim of this study was to evaluate the appropriateness of MCM in second line (2L) therapy for DLBCL by analyzing extrapolations from other cost-effectiveness analyses; to use data cuts of ZUMA-7 trial to validate MCMs and describe differences that may have arisen due to treatment specific timing and extent of cure.
A targeted literature review (TLR) was conducted to identify cost-effectiveness modeling studies that have used ZUMA-7 clinical trial data to extrapolate survival of axi-cel over the long-term. Overall survival (OS) individual patient-level data from the ZUMA-7 trial for axi-cel and historical HSoC from both primary event-free survival (EFS; median follow-up: 24.9 months) and primary OS cutoff (median follow-up: 47.2 months) were used in this analysis. Three intermediate datasets were simulated to compare differences in cure fractions and accuracy of extrapolation predictions among data cutoffs. Differences between trial arms were evaluated.
The TLR demonstrated the extrapolation using ZUMA-7 OS data cutoff provided lower difference margins with respect to long-term observed data, than external extrapolations. Within the models tested, MCM provided the best fit based on the percentage inaccuracy of extrapolations which ranged 2%-27% in the axi-cel group. Based on observed primary EFS cutoff, cure fractions ranged in both treatment arms (axi-cel: 24%-54% and HSoC: 35%-49%). The range of cure fractions narrowed following extrapolations performed on the more mature observed OS cutoff (axi-cel: 50%-54% and HSoC: 41%-50%).
Replicating prior findings, MCM methods provide the more accurate estimate of long-term OS compared with standard parametric modeling for patients with R/R DLBCL treated with CAR T-cell therapy. However, these findings also highlight the importance of obtaining appropriate clinical benchmarking to ensure clinical plausibility and face validity of long-term extrapolations intended to informed cost-effectiveness analysis and inform decision making.
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