它面临着缺乏精确的生物标志物来预测新辅助化学免疫疗法（NACI）的反应并确定患者是否应该在早期乳腺癌（BC）中使用免疫检查点抑制剂（ICIs）的困境。我们的目的是开发一种基因特征来预测 BC 患者的 NACI 反应，并确定适合添加 ICI 的个体。纳入了两个 I-SPY2 队列和一个华西医院接受 NACI 治疗的患者队列。机器学习算法用于识别关键基因。主成分分析用于计算 ImPredict (IP) 分数。基于逻辑回归分析检查生物标志物和治疗方案之间的相互作用。通过免疫组织化学（IHC）和多重 IHC 研究 IP 评分与免疫微环境之间的关系。发现队列、验证队列 1 和内部队列中 IP 评分的曲线下面积分别为 0.935、0.865 和 0.841 。标记物-治疗相互作用测试表明，免疫治疗的获益在高 IP 评分和低 IP 评分的患者之间存在显着差异（交互作用 p <0.001），并且高 IP 评分的患者更适合添加免疫治疗。我们的 IP 模型在预测方面表现出良好的性能NACI 反应是识别可从 ICI 中受益的 BC 患者的有效工具。它可以帮助临床医生优化治疗策略并指导临床决策。© 作者（或其雇主）2024。CC BY-NC 允许重复使用。禁止商业再利用。请参阅权利和权限。英国医学杂志出版。

It is encountering the dilemma of lacking precise biomarkers to predict the response to neoadjuvant chemoimmunotherapy (NACI) and determine whether patients should use immune checkpoint inhibitors (ICIs) in early breast cancer (BC). We aimed to develop a gene signature to predict NACI response for BC patients and identify individuals suitable for adding ICIs.Two I-SPY2 cohorts and one West China Hospital cohort of patients treated with NACI were included. Machine learning algorithms were used to identify key genes. Principal component analysis was used to calculate the ImPredict (IP) score. The interaction effects between biomarkers and treatment regimens were examined based on the logistic regression analysis. The relationship between the IP score and immune microenvironment was investigated through immunohistochemistry (IHC) and multiplex IHC.The area under the curves of the IP score were 0.935, 0.865, and 0.841 in the discovery cohort, validation cohort 1, and in-house cohort. Marker-treatment interaction tests indicated that the benefits from immunotherapy significantly varied between patients with high and low IP scores (p for interaction <0.001), and patients with high IP scores were more suitable for immunotherapy addition.Our IP model shows favorable performance in predicting NACI response and is an effective tool for identifying BC patients who will benefit from ICIs. It may help clinicians optimize treatment strategies and guide clinical decision-making.© Author(s) (or their employer(s)) 2024. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ.