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从HER2-阴性乳腺癌的基因表达数据预测免疫治疗的反应

Caterina A M La Porta^1,2, Ornella Garrone³, Marco Merlano⁴

¹Departement of Environmental Science and Policy, Center for Complexity and Biosystems, University of Milan, Milan, Italy. caterina.laporta@unimi.it.

Communications medicine

|October 3, 2025

概括

分析基因表达的算法ARIADNE可以预测HER2-乳腺癌患者的免疫治疗反应. 它可以识别具有较低病理完整响应率的高风险患者,有助于选择治疗.

科学领域:

在瘤学瘤学.
基因组学就是基因组学.
免疫治疗是一种免疫疗法.

背景情况:

新辅助免疫疗法对HER2和三阴性乳腺癌 (TNBC) 有希望.
病理完整反应 (pCR) 率有所不同,需要预测生物标志物.
ARIADNE是一种基于表皮细胞-介质细胞表型的算法,分析来自生物样本的基因表达.

研究的目的:

调查ARIADNE在HER2-乳腺癌患者中预测免疫治疗反应的能力.
根据对免疫疗法的预测反应,评估ARIADNE在分层患者中的表现.

主要方法:

在I-SPY 2试验中利用了HER2-乳腺癌患者的基因表达数据.
用ARIADNE算法得分将患者分为分层的低风险/高风险组.
分析了细胞因子特征,PD-L1通路相互作用和TNBC患者的单细胞数据.

主要成果:

阿里亚丹预测了差异性免疫疗法反应:高风险组的PCR为26%,低风险组的PCR为62% (OR4.7,p<0.01).
在细胞因子评分和PCR率之间发现了显著的相关性.
ARIADNE的预测能力与PD-L1通路活性有关;响应者表现出更多耗尽的T细胞.

结论:

在HER2-乳腺癌中,ARIADNE可预测免疫疗法反应,但不能预测化疗反应.
该算法有助于识别可能受益于新辅助免疫治疗的患者.
进一步的研究可以探索ARIADNE在更广泛的临床环境中进行个性化癌症治疗.

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