來自于文章：Multi-omics profiling reveals distinct microenvironment characterization and suggests immune escape mechanisms of triple-negative breast cancer 里面提到了數(shù)據(jù)：
The sequencing data is also available in GSE118527 (OncoScan), GSE76250 (HTA 2.0) and SRP157974 (WES and RNAseq)
然后同樣的作者2016年在plos one 發(fā)文重新修訂了 之前的分類，變成4類：(TNBCtype-4) tumor-specific subtypes (BL1, BL2, M and LAR)
發(fā)表在Clin Cancer Res 2015 ，貝勒醫(yī)學(xué)院研究小組的 Burstein 等人對自己的數(shù)據(jù)，198個(gè)TNBC病人芯片表達(dá)矩陣，使用80個(gè)核心基因進(jìn)行分組，得到4個(gè)TNBC的亞型。
發(fā)表在 Breast Cancer Research (2015) ：Gene-expression molecular subtyping of triple-negative breast cancer tumours: importance of immune response，數(shù)據(jù)在 GSE58812, 法國研究團(tuán)隊(duì)的等人使用 適應(yīng)性的Fuzzy-clustering 把107個(gè)TNBC 患者分成3類。
3個(gè)是 for MDSC，angiogenesis, and antigen presentation machinery
使用GSVA包的ssGSEA算法，對z-score后的RNA-seq表達(dá)矩陣進(jìn)行分析。有趣的是作者提供了RPKM矩陣哦，The RNA-seq FPKM data have been deposited at figshare (https:///10.6084/m9.figshare.7306364.v1). 所以理論上可以重現(xiàn)作者的分析。
可以把病人分成3組不同的免疫狀態(tài)，主要是看 IFNG, PD-L1, PD-1, and CD8 基因的表達(dá)
繼續(xù)看這里作者使用NBclust分類，可以把病人隊(duì)列劃分為3個(gè)類群。
分型具有生存效果
RNA-seq和HTA2.0芯片的表達(dá)數(shù)據(jù)的比較
這里使用ComBat算法抹去兩個(gè)平臺(tái)的差異
在TNBC隊(duì)列驗(yàn)證
同樣也是分成3類：
在METABRIC隊(duì)列驗(yàn)證
也可以區(qū)分成為3類，圖片在文章里面的附件！
附件圖片
Supplementary Figure 1. Workflow of our research.
Supplementary Figure 2. Estimation of the optimal clustering numbers of triple-negative breast cancer microenvironment phenotypes.
Supplementary Figure 3. Validation of microenvironment phenotypes clustering in METABRIC cohort.
Supplementary Figure 4. Validation of microenvironment phenotypes clustering in TCGA cohort.
Supplementary Figure 5. Comparison of potential molecules involved in the initiation of innate immunity among microenvironment clusters in FUSCCTNBC cohort.
Supplementary Figure 6. SNV and indel neoantigen load of the three microenvironment clusters in triple-negative breast cancer.
Supplementary Figure 7. Chromosome instability of the three microenvironment clusters in triple-negative breast cancer.
Supplementary Figure 8. Cancer testis antigen landscape of triple-negative breast cancer.
Supplementary Figure 9. Gene set enrichment analysis of enriched pathways in each cluster.
Supplementary Figure 10. Batch effect evaluation after 'Combat' of RNA-seq and HTA microarray datasets.
Supplementary Figure 11. Process and validation of mRNA clustering.
附件表格
Supplementary Table 1. The compendium of microenvironment cell subtypes in triple-negative breast cancer.
Supplementary Table 2. Correlation of estimated microenvironment cell numbers between our compendium and CIBERSORT or MCP-counter.
Supplementary Table 3. Clinicopathological characteristics of three microenvironment phenotypes in FUSCC, METABRIC and TCGA cohort.
Supplementary Table 4. Prognostic value of each cell subset by univariate Cox proportional hazards model for relapse free survival.
Supplementary Table 5. The signatures of ten oncogenic pathways.
Supplementary Table 6. Comparison of gene mutation frequency among clusters.
Supplementary Table 7. Comparison of somatic copy number alterations among clusters.
Supplementary Table 8. GO and KEGG annotation of genes in cluster-specific copy number variation peaks.
