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Are there specific transcriptional regulators, whose expression and copy number correlate with the expression of genes associated with a specific phenotype?

This recipe provides a method for identifying transcriptional regulators of a gene set associated with a specific phenotype. An example use of this recipe is a case where an investigator may want to identify determine which transcriptional regulators exhibit unique expression phenotypes (e.g. up-regulation or down-regulation). This recipe uses a procedure called "Stepwise Linkage Analysis of Microarray Signatures", first described by Adler et al. (Nat Genetics 2006). This recipe does not use the SLAMS software tool.

In particular, the phenotype is the embryonic stem cell (ESC) state, which is common to ESCs, as well as induced pluripotent stem cells (iPSCs), and also in a compendium of human cancers, such as breast cancer. In this recipe, we are interested in determining which genes transcriptionally regulate this 'stemness signature' of gene expression. This recipe recapitulates research by Wong et al., in Cell Stem Cell (2008), "Module map of stem cell genes guides creation of epithelial cancer stem cells". To recapitulate this research, we will use a procedure called Stepwise Linkage Analysis of Microarray Signatures (SLAMS), which is described by Adler et al. in Nature Genetics (2006), "Genetic regulators of large-scale transcriptional signatures in cancer". A summary description of the SLAMS procedure is listed below, and more information about SLAMS can be found in the review paper, "A SLAMS dunk for cancer regulators", by Kumar-Sinha and Chinnaiyan.

We use a gene expression dataset of primary human breast cancer tumor samples, with a complementary dataset of copy number variation data in array comparative genomic hybridization (aCGH) format, as described in Chin, K. et al, Cancer Cell, 2006. We use a set of stemness signature genes to separate breast cancer tumor samples into those which exhibit the stemness signature and those that do not by creating a module map in Genomica.  A module map characterizes the expression of the gene expression dataset, providing information about sets of genes within the dataset.

We use the classified samples (e.g. stemness signature present vs. stemness signature absent) to normalize the copy number variation data in GenePattern. Next, we identify transcriptional regulators that correlate with the changes in the copy number dataset using a gene set collection from MSigDB, in Genomica. Finally, we identify transcriptional regulators whose amplification or deletion is correlated with up- or downregulation of gene expression. We consider these genes to be 'stemness regulators', i.e. genes which regulate the genes associated with the stemness signature.

Description of the Stepwise Linkage Analysis of Microarray Signatures (SLAMS) procedure (Kumar-Sinha and Chinnaiyan):

  1. Sort tumor samples into groups based on whether the stemness signature is present (“ON”) or absent (“OFF”).
  2. Compare the DNA copy number changes between the groups of tumor samples. Calculate the association between stemness expression and CNV datasets to identify amplifications/deletions associated with the stemness signature.
  3. Select genes which are potential candidate regulators of the stemness signature, based on coordinate gene amplification/deletion and gene expression upregulation/downregulation.
  4. Validate the candidate regulators by assessing their predictive ability in independent samples of tumor samples.

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