The traditional classification of BC has utilised tumour morphology and assessment of oestrogen receptor [ER], progesterone receptor [PR] and human epidermal growth factor receptor 2 (HER2) expression.
比如一个摘要就提到:TNBC samples obtained from both TCGA (N = 150) and METABRIC (N = 320) datasets,链接是:https://ascopubs.org/doi/abs/10.1200/JCO.2019.37.15_suppl.1073?af=R
而发表于 Annals of Oncology, April 2018,的文章, 链接是,https://doi.org/10.1093/annonc/mdy024 主要是下载两个数据库总共 (n = 550) 的TNBC病人的数据进行分析
然后同样的作者,PLoS One. 2016 Jun 16;发文重新修订了 之前的分类,变成4类:(TNBCtype-4) tumor-specific subtypes (BL1, BL2, M and LAR)
发表在:Clin Cancer Res. 2015 Apr ,题目是 Comprehensive Genomic Analysis Identifies Novel Subtypes and Targets of Triple-Negative Breast Cancer,贝勒医学院研究小组的 Burstein 等人对自己的数据,198个TNBC病人芯片表达矩阵,使用80个核心基因进行分组,得到4个TNBC的亚型。数据在 GSE76275
发表在 Breast Cancer Res. 2015 Mar 2:Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response,数据在 GSE58812, 法国研究团队的等人使用 适应性的Fuzzy-clustering 把107个TNBC 患者分成3类。
中国团队发表在 Breast Cancer Res. 2016;题目是:Comprehensive transcriptome analysis identifies novel molecular subtypes and subtype-specific RNAs of triple-negative breast cancer ,使用的是[GEO:GSE76250].
发表在 J Exp Clin Cancer Res. 2018 Dec;的文章:Classification of triple-negative breast cancers based on Immunogenomic profiling. 使用的是4个公共数据集,METABRIC, TCGA, GSE75688,单细胞, and GSE103091,是两个法国队列的结合, 根据ssGSEA scores) of the 29个免疫基因集,分成HIGH,M,LOW 这3组。
中国团队发表于2019年3月的文章,题目是是:Multi-omics profiling reveals distinct microenvironment characterization and suggests immune escape mechanisms of triple-negative breast cancer 里面使用ssGSEA算法对CIBERSORT的免疫基因集进行分析,数据在 GSE76250 可以下载,把TNBC 患者分成3类。
发表在 Breast Cancer Res. 2019 May 的文章,题目是:Identification of three subtypes of triple-negative breast cancer with potential therapeutic implications. 还是前面的法国团队在 GSE58812数据集的基础上,加上GSE83937 得到238个TNBC病人,等同于GSE103091,是两个法国队列的结合
发表于 Cancers (Basel). 2019 Jul; 文章:Decoding Immune Heterogeneity of Triple Negative Breast Cancer and Its Association with Systemic Inflammation 使用的是GSE86945 数据集,使用 NMF算法,得到54 TNBC tumors were re-classified into 3 Im-Clus (ImA n = 15, ImB n = 18, ImC n = 21)