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Which genes lie in my copy number variation regions? Are these genes enriched for any biological functions or pathways?

This recipe provides a method for identifying biological functions for genes lying in copy number variation (CNV) regions. An example use of this recipe is a case where an investigator may want to examine their list of CNVs to see which genetic regions are amplified or deleted, and determine the biological functions or pathways associated with these amplified or deleted regions.

 

Copy number variations (CNVs) are large alterations to genomes, such as amplification or deletion of large segments of a chromosome. They can range in size from a focal aberration in a single gene to aberrations covering entire chromosome arms. These variations in the genome have been associated with different conditions, such as cancer. Many genomic analyses produce a set of genes which are assumed to be relevant to an underlying biological mechanism or phenotype. Thus, an investigator often has additional questions about the function or relatedness of these genes: Are they part of the same pathway? Do the gene products interact physically? Do the gene products localize to a specific part of the cell? Are the genes associated with certain stages of development? These questions, and others like them, can be answered by performing functional annotation of gene lists to better understand the underlying connections between genes.

In this particular example, we imagine a scenario in which an investigator identifies CNV regions that are amplified or deleted in glioblastoma multiforme (GBM) tumor samples, using a method called Genomic Identification of Significant Targets in Cancer (GISTIC, Mermel et al. (2011) Genome Biol.). Given a set of CNV regions, the goal is to infer which biological functions (e.g., metabolic and regulatory pathways, chemical perturbation signatures, etc.) are overrepresented in the set of reference genes that overlap with these regions. In particular, this recipe uses several Galaxy tools to find the overlap between CNV regions and reference genes obtained from the UCSC Table Browser. Then it uses the Molecular Signatures Database (MSigDB) to identify biological functions of the overlapped genes.

How can I use this recipe? This recipe may be modified to analyze CNV regions derived from any organism for which an annotated reference genome exists. Nor does the recipe depend on the algorithm used to identify these regions (GISTIC); any source of CNV data can be used. Once an investigator has pinpointed the CNV regions believed to be influencing their phenotype (disease state, cell type, etc.) of study he/she can use this recipe to identify functional pathways that may be affected by these copy number changes and draw closer to an understanding of the mechanisms behind these CNV effects.

Which genes lie in my copy number variation regions? Are there any sets of co-regulated genes in these aberrant regions?

This recipe provides a method for identifying and visualizing a network of co-regulated genes that are associated with aberrant regions identified by single nucleotide polymorphism (SNP) arrays. An example use of this recipe is a case where an investigator may want to find which genes are located in regions that exhibit significant changes (e.g. amplification or deletion) in cancer cells.

 

This recipe provides one method for identifying and visualizing aberrant regions in Diffuse Large B-Cell Lymphoma (DLBCL) cancer cells. This recipe uses copy-number variation (CNV) data from SNP arrays, and evaluates the expression of aberrant regions using a microarray dataset. Regions that are significantly changed (e.g., amplified or deleted) in cancer cells are defined by the GISTIC algorithm. In particular, this recipe makes use of several Galaxy tools to find the overlap between the aberrant regions and reference genes, and uses GenePattern to process the microarray dataset. Genomica is used to find module networks of co-regulated genes associated with these aberrant regions. A module network is a model which identifies regulatory modules from gene expression data, especially modules of co-regulated genes and their regulators. The module also identifies the conditions under which the regulation can occur.

Why analyze copy number variation regions? Copy number variations (CNVs) are large alterations to genomes, such as duplication or deletion of large segments of a chromosome. These variations in the genome have been associated with different conditions, such as cancer. In this recipe, we explore the scenario in which CNVs are elevated in a cancer cell line, and our goal is to determine the function of these duplicated genes.

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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