Sundar R,Gut. 2022 Apr;71(4):676-685. 

迄今为止，尚无预测性生物标志物来指导选择从紫杉醇中获益的胃癌(GC)患者。胃癌辅助多机构组试验(SAMIT)是一项2×2因子随机III期研究，其中GC患者被随机分配到Pac-S-1(paclitaxel+S-1)、Pac-UFT(paclitaxel+UFT) ,根治性手术后单独使用S-1或单独使用UFT，本研究的主要目的是确定预测GC患者紫杉醇化疗生存获益的基因特征

SAMIT GC样品使用定制的476基因NanoString面板进行分析。将随机森林机器学习模型应用于NanoString配置文件以开发基因特征。接受紫杉醇和雷莫芦单抗(Pac-Ram)治疗的GC转移性患者的独立队列作为外部验证队列

来自SAMIT试验的499个样品在本研究中进行分析。从Pac-S-1训练队列中，随机森林模型生成一个19基因特征，将患者分为两组：Pac-Sensitive和Pac-Resistant。在Pac-UFT验证队列中，Pac-Sensitive患者的无病生存期 (DFS) 有显着改善：3 年 DFS 分别为 66% 和 40%。在UFT或S-1单独组中，Pac敏感和Pac抗性之间没有生存差异，相互作用检验p<0.001。在外部Pac-Ram验证队列中，预测Pac-Sensitive的益处。在最大的 GC 试验 (SAMIT) 中使用机器学习技术，本文确定代表紫杉醇益处的第一个预测性生物标志物的基因特征

ABSTRACT

Objective To date, there are no predictive biomarkers to guide selection of patients with gastric cancer (GC) who benefit from paclitaxel. Stomach cancer Adjuvant Multi-Institutional group Trial (SAMIT) was a 2×2 factorial randomised phase III study in which patients with GC were randomised to Pac-S-1 (paclitaxel +S-1),Pac-UFT (paclitaxel +UFT), S-1 alone or UFT alone after curative surgery.

Design The primary objective of this study was to identify a gene signature that predicts survival benefit from paclitaxel chemotherapy in GC patients. SAMIT GC samples were profiled using a customised 476 gene NanoString panel. A random forest machine learning model was applied on the NanoString profiles to develop a gene signature. An independent cohort of metastatic patients with GC treated with paclitaxel and ramucirumab (Pac-Ram) served as an external validation cohort.

Results From the SAMIT trial 499 samples were analysed in this study. From the Pac-S-1 training cohort, the random forest model generated a 19-gene signature assigning patients to two groups: Pac-Sensitive and Pac-Resistant.In the Pac-UFT validation cohort, Pac-Sensitive patients

exhibited a significant improvement in disease free survival (DFS): 3-year DFS 66% vs 40% (HR 0.44, p=0.0029). There was no survival difference between Pac-Sensitive and Pac_x0002_Resistant in the UFT or S-1 alone arms, test of interaction p<0.001. In the external Pac-Ram validation cohort, the signature predicted benefit for Pac-Sensitive (median PFS147 days vs 112 days, HR 0.48, p=0.022).

Conclusion Using machine-learning techniques on one of the largest GC trials (SAMIT), we identify a gene signature representing the first predictive biomarker for paclitaxel benefit

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