在前面的學(xué)徒實(shí)習(xí)生數(shù)據(jù)挖掘任務(wù)列表：純R代碼實(shí)現(xiàn)ssGSEA算法評(píng)估腫瘤免疫浸潤(rùn)程度 信息描述了如何下載基因集，然后使用GSVA包進(jìn)行ssGSEA分析后可視化，為了考驗(yàn)大家學(xué)習(xí)效果，我們布置一個(gè)新的圖表復(fù)現(xiàn)：

來(lái)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)

主要是使用RNA-seq和HTA2.0芯片的表達(dá)數(shù)據(jù)，根據(jù)ssGSEA的結(jié)果對(duì)樣本進(jìn)行分組，然后講故事這樣的分組的意義。

TNBC分型歷史

看到很多媒體宣傳最難治的乳腺癌有望獲得分類(lèi)治療，但是其實(shí)TNBC分子分型的研究不少了，復(fù)旦大學(xué)邵志敏團(tuán)隊(duì)2019發(fā)表的這個(gè)中國(guó)人TNBC隊(duì)列既不是第一個(gè)，也不會(huì)是最后一個(gè)。

• 首先是2011的meta分析，把TNBC分成6類(lèi)：Basal-like 1 (BL1), basal-like 2 (BL2), immunomodulatory (IM), mesenchymal (M), mesenchymal stem-like (MSL) and luminal androgen receptor (LAR)

• 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(lèi)，變成4類(lèi)：(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(duì)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(guó)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(lèi)。

使用ssGSEA算法對(duì)CIBERSORT的免疫基因集進(jìn)行分析

本文使用的數(shù)據(jù)在 GSE76250 可以下載，分析流程如下：

發(fā)表在：Nat Commun. 2019 Apr，是中國(guó)肺癌研究領(lǐng)域比較出名的吳一龍課題組文章圖表：

使用ssGSEA算法計(jì)算26 immune cell types比例

這26個(gè)基因集來(lái)源于文章 Immunity. 2013 Oct ， 分類(lèi)如下；

• 11個(gè)是adaptive immunity

• 12個(gè)是 for innate immunity

• 3個(gè)是 for MDSC，angiogenesis, and antigen presentation machinery

使用GSVA包的ssGSEA算法，對(duì)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)

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(lèi)：

在METABRIC隊(duì)列驗(yàn)證

也可以區(qū)分成為3類(lèi)，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.