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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