Exploring the effect of cancer genomic alteration on expression with Gitools

Cancer cells often exhibit a change in number of copies of certain genomic regions when compared to normal cells (Copy Number Alterations: CNAs). Some of these CNAs may have a direct influence on the expression of genes in the affected region. The change in the number of copies of a gene may be both positive, when additional copies are gained (and the genes thus amplified) or negative, when one or more alleles of the gene are lost. The influence of CNAs on the expression of these amplified or lost genes depends on whether it occurs hetero- or homozygously and also on other regulatory factors which may override the effect of the alteration. Therefore, an essential step to verify the importance of the amplification or deletion of a given gene in the tumorigenic process is to verify if its expression tends to respond to its genomic alterations.
Effect of genomic alterations on expression

The effect of genomic alterations can be observed in the expression values. Note for example that samples with loss of CDKN2A shown lower expression values than samples without this alteration. This effect is also evident for the alteration of the other genes.

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Visualizing mutually exclusive alteration patterns in cancer with Gitools

On the quest to identify cancer driver genes, it has been observed that driver alterations that affect a pathway tend to be altered in a mutually exclusive manner. As far as we know this was first observed by Thomas et al., Nat Genet 2007, however from our experience this type of pattern can be observed in data from almost all cancer genomic projects. The rationale behind that observation is that once a gene involved in a particular critical pathway is altered, a second alteration affecting the same pathway does not confer a further selective advantage to the cancer cell. The concept of mutually exclusive alteration patterns has recently been exploited to identify cancer drivers (Ciriello et al, Genome Research 2011 and Vandin et al., Genome Research 2012).

Mutually exclusive sorting of p53-signalling pathway upstream genes

The heatmap in the left shows copy number alterations of TCGA Glioblastoma project in the KEGG TP53-signalling pathway. If sorted properly we can observe that the upstream genes show a mutual-exclusive alteration pattern, but not PTEN and CDK4. Loss in blue, gain in red.

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Finding correlated genes on TCGA expression data.

We have published a new section in IntOGen in collaboration with Hautaniemi Lab, where you can do a gene correlation with the gene expression results of TCGA (The Cancer Genome Atlas) for a given subset of patients that share some specific clinical annotations. Then you will be able to browse your correlations next to global gene expression, SNP survival and DNA methylation analyses. The data analyzed so far includes 4 tumor types from TCGA: glioblastoma multiforme (GBM; 507 samples), ovarian serous adenocarcinoma (OV; 546 samples), breast invasive carcinoma (BRCA; 525 samples) and colon adenocarcinoma (COAD; 161 samples).

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Understanding the hierarchy of your GO Terms subset

The other day I downloaded the cancer-affected Gene Ontology (GO) terms from IntOGen for up- and down-regulation via it’s Biomart interface for a few tissues. Since was only interested in the GO Cell Compartment terms, so I directly added a filter file containing all the GO CC Terms as a filter for the Biomart export.

So then… what do you do when you have a list of GO terms? Read the rest of this entry »