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重新挑战的益处评分:在免疫治疗后进展的患者的临床决策工具

Caroline Plazy¹, Mohamed Boussena², Linh Nguyen Phuong²

¹Biochemistry, Molecular Biology and Environmental Toxicology department, Grenoble-Alpes University Hospital, France; Univ. Grenoble Alpes, CNRS, UMR 5525, Grenoble INP, CHU Grenoble Alpes, TIMC, Grenoble 38000, France.

European journal of cancer (Oxford, England : 1990)

|November 23, 2025

概括

一个新的 Rechallenge Benefit Score (RBS) 预测了哪些晚期非小细胞肺癌患者在进展后受益于免疫检查点抑制剂 (ICI) 的重新挑战. 这一分数有助于指导治疗决策,当临床因素不足时.

科学领域:

在瘤学瘤学.
免疫治疗是一种免疫疗法.
机器学习在医学中的应用

背景情况:

晚期非小细胞肺癌 (NSCLC) 患者经常接受免疫检查点抑制剂 (ICI) 的第一线治疗.
在进步时存在有限的选择,使ICI重新挑战关键战略.
确定适合ICI重试的候选人仍然是一个重大的临床挑战.

研究的目的:

开发和验证免疫检查点抑制剂 (ICI) 在高级非小细胞肺癌 (NSCLC) 中重新挑战的预测得分.
确定可能从ICI重新挑战中获得临床益处的患者.
在初始ICI治疗后进展的患者指导治疗决策.

主要方法:

分析了来自80名接受ICI再试验的NSCLC患者的17项常规血液检测.
机器学习的应用,以识别在重新挑战期间与无进展生存 (rPFS) 相关的生物参数.
开发和验证与化疗和一线ICI队列相比的挑战效益评分 (RBS).

主要成果:

仅仅临床特征就不足以预测ICI复发反应.
复试效益评分 (RBS) 有效地识别了可能从ICI复试中受益的患者 (C指数为0.79).
与高RBS (1.9个月) 患者相比,低RBS患者的中位数RPFS显著更长 (6.4个月).

结论:

重试效益评分 (RBS) 被确定并验证为一种工具,用于为ICI重试选择NSCLC患者.
RBS为患者分层提供了一种可靠的方法,特别是当临床特征不足时.
这一分数可以为在先前ICI治疗后进展的患者提供治疗策略.

关键词:

免疫检查点抑制剂免疫检查点抑制剂炎症评分炎症评分炎症评分肺癌是一种肺癌.机器学习的方法是机器学习.预测性生物标志物预测性生物标志物再次挑战挑战

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