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.

Today we present to you a way to explore the effect of CNAs on gene expression on a cohort of cancer samples with the help of Gitools. To do this you first need to prepare a multi-dimensional genomic data matrix containing both CNA and expression values. We have described before how it can be loaded into Gitools – see our case study six: Studying multi-dimensional cancer data with Gitools, and our previous blog post: Exploring multiple cancer genomic alterations with Gitools.

Once you have the genomic alterations and expression data in Gitools you can see if the samples with a particular type of genomic alteration (eg. gain) have different expression values than those without this alteration. In some cases this can be easily viewed in the heatmaps (see figure above), however if we want to corroborate this observation statistically we can also use the new Group Comparisons analysis, which we have added in our 1.6.0 release of Gitools, to test if the expression values in the samples with the genomic alteration are different than in those samples without this alteration.

The guide on how to explore the effect of alteration on expression can be found as a new chapter of this case study where it is explained step by step – and it also contains the following video: