今天剖析一篇文章题目为（Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples ）
要充分理解这篇文章，需要三篇补充材料
参考文献17：Charoentong, P. et al. Pan-cancer immunogenomic analyses reveal genotypeimmunophenotype relationships and predictors of response to checkpoint blockade. Cell Rep. 18, 248–262 (2017).
参考文献18：Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
参考文献19：Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).
方法学
workflow
筛选TCGA中负荷筛选标准的患者
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患者选择和数据下载
筛选有化疗药物敏感性的TCGA的肿瘤数据
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只选择enriched for Tregs
使用参考文献17的方法，他们文章中所创建了一个database（TCIA）(q < 0.05) （q也就是FDR值）
We executed the filtering for Treg-enriched tumor samples via The Cancer Immunome Database (tcia.at) using GSEA of a non-overlapping, pancancer derived set of genes representative for Treg enrichment (FOXP3,CCL3L1, CD72, CLEC5A, ITGA4, L1CAM, LIPA, LRP1, LRRC42, MARCO,MMP12, MNDA, MRC1, MS4A6A, PELO, PLEK, PRSS23, PTGIR, ST8SIA4,STAB1).
每个cohort只要要有15samples满足帅选标准
最终只留下135 total patients for analysis across 5 TCGAcohorts (18 BLCA, 37 LUAD, 33 PAAD, 24 SKCM, 23 STAD).
聚类分析
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Treg DEGs 64个基因进行选择。这64个基因是从参考文章18的supplementary material table 中有。具体方法如下：
Significantly differentially expressed genes (DEGs) (indicated by '1') identified by comparing each cell subset with the remaining subsets, and by applying filtering as described in Online Methods.
64个基因中选择32个基因，32个基因是在纳入研究的135个患者中差异表达较大的基因（踢出了那些在纳入的135个患者中差异表达较小的gene，不剔除可能会影响结果）
对着32个基因进行k-means聚类（k=2）
Proportional significance analysis：聚类的结果和药物反应的结果（sens and res：药物使用敏感和药物使用不敏感）进行卡方检验
免疫细胞评价
- 使用cibersort对肿瘤免疫细胞浸润状态进行评估
- 按照参考文献17的方法可以机器学习的方法计算IPS（immunophenoscore ）0-10分：
a patient’s IPS can be derived in an unbiased manner using machine learning by considering the four major categories of genes that determine immunogenicity (effector cells, immunosuppressive cells, MHC molecules, and immunomodulators) by the gene expression of the cell types these comprise (e.g., activated CD4+ T cells, activated CD8+ T cells, effector memory CD4+ T cells, Tregs, MDSCs).
The IPS is calculated on a 0–10 scale based on representative cell type gene expression z-scores, where higher scores are associated with increased immunogenicity.
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按照参考文献19，探索免疫治疗相关的18个基因的聚类结果，观察聚类的结果是否和32个基因的聚类结果有相似性。
在参考文献19中，在554个候选基因中，采取2CT-CRISRP这种较为高大上的方法帅选出了19个和免疫治疗相关的基因。
DNA可及性分析：
结果
结果一很简单：对135进行聚类，再拆分不同的癌肿进行聚类
其中cluster1 和cluster2能够很好的反应sens组和res组。卡方检验P=0.0007
图b-f只有SKCM和STAD两种癌症的卡方值P<0.05
结果二也很简单
A为cluster1和cluster2的生存分析，B为cluster1中res的患者和cluster2中res的患者。
说明了这种32个基因的expression signature可以较好的区分不同临床表现的患者
结果三：
a-e比较cluster1 和cluster2中CD8A和CD8B，HLA-A，PRF1的表达量还有比较两组cibersore免疫细胞abandance的结果。
表一是对cibersort图片的补充。
f-j比较cluster1中res的患者和cluster2中res的患者的CD8A和CD8B，HLA-A，PRF1的表达量还有比较两组cibersore免疫细胞abandance的结果。
结果四：验证队列OS的比较，结果全部重现一遍
结果五：与免疫治疗marker相关的分析
A是IPS评分，
B-C是PD-1和CTLA4的表达，
D：使用参考文献19的18个免疫治疗相关的基因再次进行聚类分析kmeans（K=2），对比32个基因的聚类分析的结果，发现异质性=0.54.E:在这18个基因中cluster1中高表达的占了12个。热图体现。
文章结论
这个就自己体会了
our results reveal a gene signature able to produce unsupervised clusters of Treg-enriched patients, with one cluster of patients uniquely representative of an immunogenic tumor microenvironment. Ultimately, these results support the proposed gene signature as a putative biomarker to identify certain Treg-enriched patients with immunogenic tumors that are more likely to be associated with features of favorable clinical outcome.
