Immunotherapy has been applied to patients with breast cancer. However, only part of patients benefits from the current immunotherapy. Accurate prediction of individual response to immunotherapy can be beneficial for breast cancer management. CD8⁺ T cells are the main force of anti-tumor immunity. This study aimed to establish a CD8⁺ T cell-related gene expression signature for prediction of breast cancer prognostic and immunotherapy efficacy. RNA-seq transcriptomic data was the basics of this research. Weighted gene co-expression network analysis (WGCNA) and the least absolute shrinkage and selection operator (LASSO) Cox regression analysis established the prognostic signature. We identified 290 CD8⁺ T cell-related genes in the training set and established a risk-score model based on 8-genes panel (SOCS1, IL10, CAMK4, CXCL13, KIR2DS4, TESPA1, CD70 and ICAM4). Subsequently, univariate Cox regression analysis suggested that high risk-score was a risk factor for breast cancer (HR = 3.1, 95%CI 2.0–4.8, P < 0.001). In tumor microenvironment, high-risk tumors present decreased tumor infiltrating CD8⁺ T cells and increased M2 macrophages. The low-risk patients may benefit more from immune checkpoint blockade immunotherapy than the high-risk patients. Moreover, breast tumors which sensitive to immune checkpoint inhibitor (ICI) showed higher IL10 expression.

免疫疗法已应用于乳腺癌患者。然而，只有部分患者从目前的免疫疗法中受益。准确预测个体对免疫治疗的反应可能有益于乳腺癌管理。CD8 ⁺ T细胞是抗肿瘤免疫的主力军。本研究旨在建立 CD8 ⁺ T 细胞相关基因表达特征，用于预测乳腺癌预后和免疫治疗疗效。RNA-seq 转录组数据是这项研究的基础。加权基因共表达网络分析 (WGCNA) 和最小绝对收缩和选择算子 (LASSO) Cox 回归分析建立了预后特征。我们在训练集中识别了 290 个 CD8 ⁺ T 细胞相关基因，并建立了基于 8 基因组（SOCS1、IL10、CAMK4、CXCL13、KIR2DS4、TESPA1、CD70和ICAM4）的风险评分模型。随后，单变量 Cox 回归分析表明高风险评分是乳腺癌的危险因素（HR = 3.1，95% CI 2.0–4.8，P < 0.001）。在肿瘤微环境中，高危肿瘤呈现肿瘤浸润CD8 ⁺ T细胞减少和M2巨噬细胞增加。低风险患者可能比高风险患者从免疫检查点阻断免疫治疗中获益更多。此外，对免疫检查点抑制剂（ICI）敏感的乳腺肿瘤表现出较高的IL10表达。