Department of Breast Surgical Oncology, National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, No.17 Panjiayuan Nanli, Chaoyang District, Beijing, 100021, China.
Department of Thoracic Surgery, Beijing Tuberculosis and Thoracic Tumor Research Institute/Beijing Chest Hospital, Capital Medical University, Beijing, China.
Breast Cancer Res. 2023 Jun 7;25(1):63. doi: 10.1186/s13058-023-01669-6.
Breast cancer presents as one of the top health threats to women around the world. Myeloid cells are the most abundant cells and the major immune coordinator in breast cancer tumor microenvironment (TME), target therapies that harness the anti-tumor potential of myeloid cells are currently being evaluated in clinical trials. However, the landscape and dynamic transition of myeloid cells in breast cancer TME are still largely unknown.
Myeloid cells were characterized in the single-cell data and extracted with a deconvolution algorithm to be assessed in bulk-sequencing data. We used the Shannon index to describe the diversity of infiltrating myeloid cells. A 5-gene surrogate scoring system was then constructed and evaluated to infer the myeloid cell diversity in a clinically feasible manner.
We dissected the breast cancer infiltrating myeloid cells into 15 subgroups including macrophages, dendritic cells (DCs), and monocytes. Mac_CCL4 had the highest angiogenic activity, Mac_APOE and Mac_CXCL10 were highly active in cytokine secretion, and the DCs had upregulated antigen presentation pathways. The infiltrating myeloid diversity was calculated in the deconvoluted bulk-sequencing data, and we found that higher myeloid diversity was robustly associated with more favorable clinical outcomes, higher neoadjuvant therapy responses, and a higher rate of somatic mutations. We then used machine learning methods to perform feature selection and reduction, which generated a clinical-friendly scoring system consisting of 5 genes (C3, CD27, GFPT2, GMFG, and HLA-DPB1) that could be used to predict clinical outcomes in breast cancer patients.
Our study explored the heterogeneity and plasticity of breast cancer infiltrating myeloid cells. By using a novel combination of bioinformatic approaches, we proposed the myeloid diversity index as a new prognostic metric and constructed a clinically practical scoring system to guide future patient evaluation and risk stratification.
乳腺癌是全球女性面临的最大健康威胁之一。髓系细胞是乳腺癌肿瘤微环境（TME）中最丰富的细胞和主要免疫协调细胞，目前正在临床试验中评估利用髓系细胞抗肿瘤潜力的靶向治疗方法。然而，乳腺癌 TME 中髓系细胞的全貌和动态转变在很大程度上仍不清楚。
在单细胞数据中对髓系细胞进行特征分析，并使用去卷积算法从批量测序数据中提取进行评估。我们使用香农指数来描述浸润性髓系细胞的多样性。然后构建并评估了一个 5 基因替代评分系统，以便以临床可行的方式推断髓系细胞的多样性。
我们将乳腺癌浸润性髓系细胞分为 15 个亚群，包括巨噬细胞、树突状细胞（DC）和单核细胞。Mac_CCL4 具有最高的血管生成活性，Mac_APOE 和 Mac_CXCL10 在细胞因子分泌方面活性很高，而 DC 则上调了抗原呈递途径。在去卷积批量测序数据中计算了浸润性髓系细胞的多样性，我们发现髓系细胞多样性越高，与更好的临床结局、更高的新辅助治疗反应率和更高的体细胞突变率显著相关。然后，我们使用机器学习方法进行特征选择和降维，生成了一个由 5 个基因（C3、CD27、GFPT2、GMFG 和 HLA-DPB1）组成的临床友好评分系统，可用于预测乳腺癌患者的临床结局。
本研究探讨了乳腺癌浸润性髓系细胞的异质性和可塑性。通过使用一种新的生物信息学方法组合，我们提出了髓系细胞多样性指数作为一种新的预后指标，并构建了一个临床实用的评分系统，以指导未来的患者评估和风险分层。
