Visualizing mutually exclusive alteration patterns in cancer with Gitools

//Visualizing mutually exclusive alteration patterns in cancer with Gitools

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.

Since this feature is so striking we thought it was useful to incorporate a new sorting option in Gitools to automatically sort a heatmap by mutually exclusive patterns. This makes it easy to find mutually exclusive altered genes in a heatmap of genomic alterations in several tumors. With the new version of Gitools we released recently (Gitools 1.6.0) this features is now available to everyone.

Coupled with this post we have added a further tutorial (Finding and visualizing mutually exclusive genes) to our latest Case Study to show how to we take advantage of this feature using a clear example from the TCGA Glioblastoma data. Please see the figure and read the legend for details and watch the video tutorial to understand how it is done.

More tutorials which will present other new features are to come soon, so stay tuned! Also you may want to read the previous blog post (Exploring multiple cancer genomics alterations with Gitools) as an introduction to this post and the Case Study.

 

 

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