Comprehensive identification of mutational cancer driver genes across 12 tumor types

The Cancer Genome Atlas (TCGA) is an initiative supported by the National Cancer Institute and the National Human Genome Research Institute. Its immediate aim is to obtain a catalog of the molecular changes that occur in hundreds of tumors from the more common cancers in population. One year ago, the Pan-Cancer analysis group was formed within the TCGA, with the goal of analyzing alterations surveyed across several cancer types with an integrative approach. The rationale was that such across tumor types integrative analysis would provide insights to the process of tumorigenesis that could not be obtained from the analysis of each dataset separately. As explained in a previous post, we have had the opportunity to participate in this exciting project, whose marker paper has been recently published in Nature Genetics. On detail, our role was to analyze the mutations of the Pan-Cancer data set to identify genes that drive tumorigenesis in one or more of these cancer types. The results of this analysis have been published today in Nature Scientific Reports.

The Cancer Genome Atlas Pan-Cancer Project

Yesterday the paper describing TCGA Pan-Cancer Project was published in Nature Genetics. We’ve had the opportunity to participate in this exciting project and here I would like to explain our experience and contribution to it.

 

We have been interested for quite a while in the study of patterns of genomics alterations in cancer across tumor types. Thus a project like the TCGA Pan-Cancer provided a unique opportunity to apply our tools and expertise to a unique collection of data.

 

In the past few years we have developed computational methodologies to identify cancer drivers by analyzing the patterns of somatic mutations across tumors (i.e OncodriveFM and OncodriveCLUST) as well as tools to facilitate the visual exploration of multidimensional cancer genomics datasets (i.e. GitoolsIntOGen, see our review on this topic if you are interested in this), we now had the opportunity to apply those tools to TCGA Pan-Cancer data.

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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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