Table2_Identification of tumor antigens and immunogenic cell death-related subtypes for the improvement of immunotherapy of breast cancer.XLSX

The current immunotherapy strategy for breast cancer is limited. Tumor neoantigens have been proven to be a promising biomarker and potential target of immunotherapy in a variety of tumors. However, their effectiveness for breast cancer remains unclear. Immunogenic cell death (ICD) is a regulated form of cell death that can reshape the tumor immune microenvironment and activate adaptive immune responses. To this end, we screened potential antigens that could be used both for the development of immunotherapy and differentiating the patient-specific immune responses based on ICD-related risk signatures, in order to formulate an accurate scheme for breast cancer immunotherapy. We retrieved the gene expression profiles of the breast invasive cancer cohort and their corresponding clinical control data from The Cancer Genome Atlas. The Gene Expression Profiling Interactive Analysis (GEPIA) database was used to evaluate tumor antigen expression, the cBioPortal program was used to identify genetic variations, and the TIMER website was used to estimate the immune infiltration signatures. The risk score predictive model based on the ICD-related genes was constructed using the least absolute shrinkage and selection operator (LASSO) Cox regression algorithm, and the cohort was divided into low- and high-risk score groups. Two tumor antigens, namely, CCNE1 and PLK1, were associated with poor prognosis and infiltration of antigen-presenting cells. Furthermore, the ICD-related risk signature could significantly predict survival outcomes. The risk groups based on the ICD-related signature predictive model showed diverse immune infiltration and molecular and clinical features. The high-risk group was associated with low immune cell infiltration, immune score, expression of immune checkpoints, and human leukocyte antigen genes but high levels of CCNE1 and PLK1 and poor survival outcome. In conclusion, CCNE1 and PLK1 were identified as potential antigens in breast cancer. The ICD-related prognostic model distinguished immune response heterogeneity and predicted prognosis. Patients with high ICD-related risk scores were suitable to receive combination treatments based on CCNE1 or PLK1 and immune checkpoint inhibitors. In the future, these results will help us develop more accurate treatment schemes for patients with breast cancer.

当前针对乳腺癌的免疫治疗策略尚显局限。肿瘤新抗原已被证实为多种肿瘤免疫治疗的潜在生物标志物和靶点。然而，其在乳腺癌治疗中的有效性尚不明确。免疫原性细胞死亡（ICD）是一种受调控的细胞死亡形式，能够重塑肿瘤免疫微环境并激活适应性免疫反应。为此，我们筛选出可能用于免疫治疗开发以及基于ICD相关风险特征的病人特异性免疫反应区分的潜在抗原，旨在制定针对乳腺癌免疫治疗的精确方案。我们从癌症基因组图谱中检索了乳腺癌侵袭性癌症队列及其相应的临床对照数据。使用基因表达分析互动数据库（GEPIA）评估肿瘤抗原表达，利用cBioPortal程序识别遗传变异，并通过TIMER网站估算免疫浸润特征。基于ICD相关基因的风险评分预测模型通过最小绝对收缩和选择算子（LASSO）Cox回归算法构建，将队列分为低风险和高风险评分组。两种肿瘤抗原，即CCNE1和PLK1，与不良预后和抗原呈递细胞浸润相关。此外，ICD相关风险特征显著预测生存结果。基于ICD相关特征预测模型的风险组显示出多样的免疫浸润、分子和临床特征。高风险组与低免疫细胞浸润、免疫评分、免疫检查点表达和人类白细胞抗原基因水平相关，但CCNE1和PLK1水平较高，且预后较差。总之，CCNE1和PLK1被确认为乳腺癌的潜在抗原。ICD相关预后模型区分了免疫反应异质性并预测了预后。具有高ICD相关风险评分的患者适合接受基于CCNE1或PLK1及免疫检查点抑制剂的联合治疗。未来，这些结果将有助于我们为乳腺癌患者开发更精确的治疗方案。