Comparative Efficacy of Lenvatinib Plus Immunotherapy and Regorafenib Plus Immunotherapy After Lenvatinib Failure for Advanced Hepatocellular Carcinoma: A Retrospective Study.

IF 1.9 Q3 PHARMACOLOGY & PHARMACY Drugs - Real World Outcomes Pub Date : 2025-03-01 Epub Date: 2025-01-20 DOI:10.1007/s40801-024-00480-9
Zeyu Yu, Bin Leng, Ran You, Lingfeng Diao, Qingyu Xu, Guowen Yin
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Abstract

Background: The combination of regorafenib and immune checkpoint inhibitor (ICI) has been the most popular second-line systemic therapy for advanced hepatocellular carcinoma (HCC). However, considering the good anti-tumor performance of lenvatinib, combined immunotherapy on the basis of lenvatinib after first-line lenvatinib failure is also popular in clinical practice. This study aimed to compare the efficacy and safety of regorafenib plus ICI (TACE-R-I) versus lenvatinib plus ICI (TACE-L-I) in patients with advanced HCC after lenvatinib failure.

Methods: In this single-center retrospective study, 164 patients with advanced HCC were enrolled from January 2019 to March 2024 in China. All patients were aged ≥ 18 years, clinically or pathologically diagnosed with HCC. All patients received trans-arterial chemoembolization (TACE) as local treatment. Overall survival (OS), progression-free survival (PFS), and treatment-related adverse events (TRAEs) were compared between groups. The Cox regression model was used to analyze the factors associated with OS and PFS.

Results: We compared 77 patients from each group after propensity score matching (PSM). There was no significant difference in the OS (p = 0.255) or PFS (p = 0.387) between groups. However, in the subgroup (distant metastases, Barcelona Clinic Liver Cancer (BCLC) stage C or tumor thrombus), the TACE-R-I group showed better survival benefit than the TACE-L-I group. The multivariable Cox regression model suggested that BCLC stage and alpha-fetoprotein (AFP) were independently associated with OS. Distant metastases, tumor thrombus and Child-Pugh were independent associated factors for PFS (p < 0.05). The frequency of grade ≥ 3 TRAEs was not significantly different between groups (p ≥ 0.05).

Conclusion: Our study demonstrated that in patients with greater tumor burden, the TACE-R-I group showed better OS and PFS benefits than the TACE-L-I group. However, in the overall population of HCC patients, there was no significant difference in efficacy and safety between the groups.

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Lenvatinib治疗晚期肝细胞癌失败后Lenvatinib联合免疫治疗与Regorafenib联合免疫治疗的疗效比较:一项回顾性研究。
背景:瑞非尼联合免疫检查点抑制剂(ICI)已成为晚期肝细胞癌(HCC)最流行的二线全身治疗。然而,考虑到lenvatinib良好的抗肿瘤性能,在一线lenvatinib失败后,在lenvatinib的基础上联合免疫治疗在临床中也很流行。本研究旨在比较reorafenib + ICI (TACE-R-I)与lenvatinib + ICI (TACE-L-I)在lenvatinib失效后晚期HCC患者中的疗效和安全性。方法:在这项单中心回顾性研究中,于2019年1月至2024年3月在中国招募了164例晚期HCC患者。所有患者年龄≥18岁,临床或病理诊断为HCC。所有患者均接受经动脉化疗栓塞(TACE)作为局部治疗。比较两组患者的总生存期(OS)、无进展生存期(PFS)和治疗相关不良事件(TRAEs)。采用Cox回归模型分析影响OS和PFS的相关因素。结果:经倾向评分匹配(PSM)后,两组共77例患者进行比较。两组间OS (p = 0.255)和PFS (p = 0.387)差异无统计学意义。然而,在亚组(远处转移,巴塞罗那临床肝癌(BCLC) C期或肿瘤血栓)中,TACE-R-I组的生存获益优于TACE-L-I组。多变量Cox回归模型提示BCLC分期和甲胎蛋白(AFP)与OS独立相关。远处转移、肿瘤血栓和Child-Pugh是PFS的独立相关因素(p结论:我们的研究表明,在肿瘤负担较大的患者中,TACE-R-I组比TACE-L-I组具有更好的OS和PFS益处。然而,在HCC患者的总体人群中,两组之间的疗效和安全性没有显著差异。
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来源期刊
Drugs - Real World Outcomes
Drugs - Real World Outcomes PHARMACOLOGY & PHARMACY-
CiteScore
3.60
自引率
5.00%
发文量
49
审稿时长
8 weeks
期刊介绍: Drugs - Real World Outcomes targets original research and definitive reviews regarding the use of real-world data to evaluate health outcomes and inform healthcare decision-making on drugs, devices and other interventions in clinical practice. The journal includes, but is not limited to, the following research areas: Using registries/databases/health records and other non-selected observational datasets to investigate: drug use and treatment outcomes prescription patterns drug safety signals adherence to treatment guidelines benefit : risk profiles comparative effectiveness economic analyses including cost-of-illness Data-driven research methodologies, including the capture, curation, search, sharing, analysis and interpretation of ‘big data’ Techniques and approaches to optimise real-world modelling.
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