阿基仑赛对比历史治疗用于亚裔复发/难治性弥漫大 B 细胞淋巴瘤的疗效：ZUMA-1 与 REAL-TREND 的匹配调整间接比较

To better understand the comparative effectiveness of axicabtagene ciloleucel (axi-cel) to historical standard of care (SoC) for the treatment of refractory diffused large B-cell lymphoma (DLBCL) among patients of Asian descent, we conducted a matching-adjusted indirect treatment comparison (MAIC) of the pivotal ZUMA-1 trial (NCT02348216) and the Asia-based REAL-TREND cohort. The individual patient data (IPD) from ZUMA-1 (n = 101 infused patients) used the 60-month data-cut, while the REAL-TREND cohort data consisted of aggregate data and pseudo-IPD derived from digitized curves. The outcomes were overall survival (OS), complete response (CR) and overall response rate (ORR), as reported in REAL-TREND. The MAIC weights were derived using age 60, sex, proportion of fourth line or higher (4L+) patients, international prognostic index (IPI; 0-1 vs. 2 vs. 3 vs. 4-5), and refractory to SCT. Sensitivity analyses explored the use of the intention to treat population, alternative variable alignment and use of central review assessed response.

Some baseline characteristics were similar across ZUMA-1 and REAL-TREND, such as age and IPI scores, but key differences included proportion of 4L + patients (69% vs. 9%), of ECOG performance score 0-1 (100% vs. 59%), and type of refractoriness. The MAIC models aligned on prior lines and refractoriness, while differences in ECOG performance scores were captured through IPI. The resulting effective sample size for ZUMA-1 was 31. 1. The estimated hazard ratio for OS was 0. 27 (95% confidence interval [CI]: 0.

15-0. 50) and sensitivity analyses led to similar estimates. Strong effects were estimated for both ORR (odds ratio: 20. 76; 95% CI: 7. 18-60. 02) and CR (odds ratio: 15. 25; 95% CI: 6. 84-33. 98). The comparative efficacy of axi-cel relative to historical SoC was similar to that observed in studies restricted to Western settings. By providing outcomes data within a population of Asian-descent, we can provide better economic modeling to support reimbursements and improved access.