第01周-探究為何一個TNBC對gefitinib過於敏感
探究為何一個TNBC對gefitinib過於敏感
背景知識
- EGFR inhibition by gefitinib
- triple-negative breast cancer (TNBC)
- patient-derived xenografts (PDXs)
- Deep single-cell RNAsequencing of 3,500 cells
實驗設計
作者製作了一批TNBCs (15/18)的PDX模型,然後用這些模型來測試其對 EGFR inhibitor gefitinib 敏感情況。前人報道該藥物在TNBC病人裡面有效率是38.7%,與他們的實驗想符合(6/18), 但是其中有一個人的反映比較特殊,就是 GCRC1735, **一個70歲的老奶奶,該藥物治療效果出奇的好。**實驗表明是正中靶點,降低了pEGFR,同時下調了pERK通路,激活了凋亡通路。基因檢測表明該老奶奶有一個 pathogenic BRCA1
單細胞轉錄組測序
因為bulk測序無法解決問題
To understand functional properties associated with heterogeneous EGFR expression in an unbiased manner, single cell RNA-seq was performed on freshly dissociated cells from the PDX (3,483 cells, with an average of 40,564 unique molecular identifiers (UMIs) and 5,146 genes detected per cell)
資料都在SRA資料庫裡面,如下:
其中單細胞測序是對一個70歲的老奶奶做的,所有的資料都在SRP110989裡面,共3500個細胞,分析結果如下:
Cells partitioned into six subpopulations
- a mesenchymal/stem (M/S) cluster ,表現為 EGFR 和mesenchymal相關基因高表達
- the nuclear/mitochondrial cluster,表現為 nuclear 和 mitochondrial基因定位
- a proliferative cluster,表現為細胞週期相關基因高表達
- the basal cluster ,包括 (KRT6A/B
- A transitional cluster , 差異基因太少
subpopulation-specific markers
用 diffuse t-SNE 進行分組後,找到 marker 基因
pathway analysis
對找到的marker基因進行註釋
分析了5個公共資料:
上面得到的各個cluster 的 gene list需要跟公共資料進行對比註釋
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BRCA1 and familial breast cancers (GSE49481)(Larsen et al., 2014)
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pre/post-chemotherapy (GSE32072)(Gonzalez-Angulo et al., 2012)
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FACS normal mammary cell (GSE16997)(Lim et al., 2009)
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(GSE37223)(Kannan et al., 2013)
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FACS normal mammary cells single-cell qPCR (GSE70554)(Lawson et al., 2015).
主要是看差異表達,還有用 GOBO 演算法看 signature scores
使用 Monocle 進行 trajectory 推斷
這個是演算法問題,可以先略過,直接看結論。
補充材料講解了WES,scRNA-seq資料分析
哪些軟體,哪些步驟! 尤其注意我標記的地方。
乳腺癌表達分型
拿到了乳腺癌患者的表達矩陣,就可以根據ssp2003 , ssp2006 and pam50來對其進行分類
Breast cancers were divided into luminal A, luminal B, HER2+ and basal-like subtypes using the ssp2003 , ssp2006 and pam50 classi ers, via the ‘genefu’ R package (http://www.bioconductor.org/packages/release/bioc/ html/genefu.html).
參考文獻是:
- Sorlie,T. et al. (2003) Repeated observation of breast tumor subtypes inindependent gene expression data sets. Proc. Natl Acad. Sci. USA,
- Hu,Z. et al. (2006) The molecular portraits of breast tumors are conserved across microarray platforms. BMC Genomics
- Parker,J.S. et al. (2009) Supervised risk predictor of breast cancer based onintrinsic subtypes. J. Clin. Oncol.
乳腺癌整合生存分析網頁工具
The Kaplan Meier plotter is capable to assess the effect of 54,675 genes on survival using 10,461 cancer samples. These include 5,143 breast, 1,816 ovarian, 2,437 lung and 1,065 gastric cancer patients with a mean follow-up of 69 / 40 / 49 / 33 months. Primary purpose of the tool is a meta-analysis based biomarker assessment.
Characteristics | Number of tumors |
---|---|
All tumors | 1881 |
ER+ tumors | 1225 |
ER- tumors | 395 |
Untreated tumors | 927 |
TAM treated tumors | 326 |
(文章轉自jimmy的2018年閱讀文獻筆記)