今天剖析一篇文章題目為（Identification of an immune gene expression signature associated with favorable clinical features in Treg-enriched patient tumor samples ）
要充分理解這篇文章烈钞，需要三篇補(bǔ)充材料
參考文獻(xiàn)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).
參考文獻(xiàn)18：Newman, A. M. et al. Robust enumeration of cell subsets from tissue expression profiles. Nat. Methods 12, 453–457 (2015).
參考文獻(xiàn)19：Patel, S. J. et al. Identification of essential genes for cancer immunotherapy. Nature 548, 537–542 (2017).
方法學(xué)
workflow
篩選TCGA中負(fù)荷篩選標(biāo)準(zhǔn)的患者
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患者選擇和數(shù)據(jù)下載
篩選有化療藥物敏感性的TCGA的腫瘤數(shù)據(jù)
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只選擇enriched for Tregs
使用參考文獻(xiàn)17的方法，他們文章中所創(chuàng)建了一個(gè)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).
每個(gè)cohort只要要有15samples滿足帥選標(biāo)準(zhǔn)
最終只留下135 total patients for analysis across 5 TCGAcohorts (18 BLCA, 37 LUAD, 33 PAAD, 24 SKCM, 23 STAD).
聚類分析
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Treg DEGs 64個(gè)基因進(jìn)行選擇湿故。這64個(gè)基因是從參考文章18的supplementary material table 中有。具體方法如下：
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個(gè)基因中選擇32個(gè)基因酌摇，32個(gè)基因是在納入研究的135個(gè)患者中差異表達(dá)較大的基因（踢出了那些在納入的135個(gè)患者中差異表達(dá)較小的gene鉴吹，不剔除可能會(huì)影響結(jié)果）
對著32個(gè)基因進(jìn)行k-means聚類（k=2）
Proportional significance analysis：聚類的結(jié)果和藥物反應(yīng)的結(jié)果（sens and res：藥物使用敏感和藥物使用不敏感）進(jìn)行卡方檢驗(yàn)
免疫細(xì)胞評價(jià)
- 使用cibersort對腫瘤免疫細(xì)胞浸潤狀態(tài)進(jìn)行評估
- 按照參考文獻(xiàn)17的方法可以機(jī)器學(xué)習(xí)的方法計(jì)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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按照參考文獻(xiàn)19，探索免疫治療相關(guān)的18個(gè)基因的聚類結(jié)果谊迄，觀察聚類的結(jié)果是否和32個(gè)基因的聚類結(jié)果有相似性闷供。
在參考文獻(xiàn)19中，在554個(gè)候選基因中统诺，采取2CT-CRISRP這種較為高大上的方法帥選出了19個(gè)和免疫治療相關(guān)的基因歪脏。
DNA可及性分析：
結(jié)果
結(jié)果一很簡單：對135進(jìn)行聚類篙议，再拆分不同的癌腫進(jìn)行聚類
其中cluster1 和cluster2能夠很好的反應(yīng)sens組和res組唾糯。卡方檢驗(yàn)P=0.0007
圖b-f只有SKCM和STAD兩種癌癥的卡方值P<0.05
結(jié)果二也很簡單
A為cluster1和cluster2的生存分析鬼贱，B為cluster1中res的患者和cluster2中res的患者移怯。
說明了這種32個(gè)基因的expression signature可以較好的區(qū)分不同臨床表現(xiàn)的患者
結(jié)果三：
a-e比較cluster1 和cluster2中CD8A和CD8B，HLA-A这难，PRF1的表達(dá)量還有比較兩組cibersore免疫細(xì)胞abandance的結(jié)果舟误。
表一是對cibersort圖片的補(bǔ)充。
f-j比較cluster1中res的患者和cluster2中res的患者的CD8A和CD8B姻乓，HLA-A嵌溢，PRF1的表達(dá)量還有比較兩組cibersore免疫細(xì)胞abandance的結(jié)果。
結(jié)果四：驗(yàn)證隊(duì)列OS的比較蹋岩，結(jié)果全部重現(xiàn)一遍
結(jié)果五：與免疫治療marker相關(guān)的分析
A是IPS評分赖草，
B-C是PD-1和CTLA4的表達(dá)，
D：使用參考文獻(xiàn)19的18個(gè)免疫治療相關(guān)的基因再次進(jìn)行聚類分析kmeans（K=2）剪个，對比32個(gè)基因的聚類分析的結(jié)果秧骑，發(fā)現(xiàn)異質(zhì)性=0.54.E:在這18個(gè)基因中cluster1中高表達(dá)的占了12個(gè)。熱圖體現(xiàn)。
文章結(jié)論
這個(gè)就自己體會(huì)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.
