Department of Medical 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.
Biomed Res Int. 2021 Jun 1;2021:6650107. doi: 10.1155/2021/6650107. eCollection 2021.
To better understand the immune-related heterogeneity of tumor microenvironment (TME) and establish a prognostic model for breast cancer in clinical practice.
For the 2620 breast cancer cases obtained from The Cancer Genome Atlas and the Molecular Taxonomy of Breast Cancer International Consortium, the CIBERSORT algorithm was performed to identify the immunological pattern, which underwent consensus clustering to curate TME subtypes, and biological profiles were explored by enrichment analysis. Random forest analysis, least absolute shrinkage, and selection operator analysis, in addition to uni- and multivariate COX regression analyses, were successively employed to precisely select the significant genes with prediction values for the introduction of the prognostic model.
Three TME subtypes with distinct molecular and clinical features were identified by an unsupervised clustering approach, of which the molecular heterogeneity could be the result of cell cycle dysfunction and the variation of cytotoxic T lymphocyte activity. A total of 15 significant genes were proposed to construct the prognostic immune-related score system, and a predictive model was established in combination with clinicopathological characteristics for the survival of breast cancer patients. For immunological signatures, proactivity of CD8 T lymphocytes and hyperangiogenesis could be attributed to heterogeneous survival profiles.
We developed and validated a prognostic model based on immune-related signatures for breast cancer. This promising model is justified for validation and optimized in future clinical practice.
更好地了解肿瘤微环境（TME）的免疫相关异质性，并在临床实践中建立乳腺癌的预后模型。
对从癌症基因组图谱和乳腺癌国际联合会的分子分类学中获得的 2620 例乳腺癌病例，使用 CIBERSORT 算法识别免疫模式，进行共识聚类以策展 TME 亚型，并通过富集分析探索生物学特征。随机森林分析、最小绝对值收缩和选择算子分析，以及单变量和多变量 COX 回归分析，被依次用于精确选择具有预测值的显著基因，以引入预后模型。
通过无监督聚类方法鉴定出三种具有不同分子和临床特征的 TME 亚型，其中分子异质性可能是细胞周期功能障碍和细胞毒性 T 淋巴细胞活性变化的结果。提出了总共 15 个显著基因，用于构建预后免疫相关评分系统，并结合临床病理特征为乳腺癌患者的生存建立预测模型。对于免疫特征，CD8 T 淋巴细胞的活性和高血管生成可归因于不同的生存特征。
我们开发并验证了基于乳腺癌免疫相关特征的预后模型。该有前途的模型需要在未来的临床实践中进行验证和优化。
