Computational protocol: Overcoming endocrine resistance due to reduced PTEN levels in estrogen receptor-positive breast cancer by co-targeting mammalian target of rapamycin, protein kinase B, or mitogen-activated protein kinase kinase

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Protocol publication

[…] The original RNA-seq data of MCF7L-shPTEN cells were deposited into the GEO database (GSE53300). TCGA data were accessed from the TCGA data portal [],[]. The mega-set Compendium of breast cancer gene expression profiles was previously reported []. RNA samples were extracted by using Qiagen (Germantown, MD, USA) RNeasy Mini kit and labeled with Illumina (San Diego, CA, USA) TruSeq RNA kit. Next-generation RNA-seq was performed in Illumina RNA-seq platform and scanned by HiSeq 2000. We used the freely available Cufflinks/Cuffdiff software package (v1.3.0) to identify differences in expression of genes/isoforms between the two samples []. In brief, this approach employs a statistical approach based on Jensen-Shannon divergence, looking for differences in the distribution of expression of isoforms between the sample sets. Differentially expressed genes between PTEN-wild-type (WT) and -KD cells were chosen by FDR <0.05 and their values were represented by Java TreeView []. Pearson’s correlation (represented as a t statistic or `t score’) was used as previously described [],[], in order to assess the global similarity of gene patterns between PTEN-low and other known gene signatures. Gene set enrichment analysis was performed by one-sided Fisher’s exact test (represented as two-sided Fisher’s z score). To score each human breast tumor expression profile for similarity to the PTEN-low gene signature, a `t score’ was derived for the tumor in relation to the PTEN-low signature patterns, as previously described [],[]. […]

Pipeline specifications

Software tools Cufflinks, TreeView
Application RNA-seq analysis
Organisms Homo sapiens
Diseases Breast Neoplasms, Neoplasms
Chemicals Estrogens, Phosphatidylinositols, Sirolimus