What is it about?
We show how to model several molecular data sets with a computational method to identify cancer driver genes. Our method, AMARETTO, successfully combines genome wide measurements of DNA copy number, DNA methylation and gene expression to hone down on the genes that are truly involved in driving oncogenesis. Furthermore AMARETTO also links the identified cancer driver genes with their downstream targets.
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Why is it important?
AMARETTO provides a new method to make sense of multi-omics data of cancer patients. Instead of millions of data points, AMARETTO reduces the complexity to 100 modules representing the most relevant processes representing the active oncogenic pathways. Each module is further associated with a set of cancer driver genes that were selected based on their multi-omics aberrations.
Read the Original
This page is a summary of: Identification of ovarian cancer driver genes by using module network integration of multi-omics data, Interface Focus, June 2013, Royal Society Publishing,
DOI: 10.1098/rsfs.2013.0013.
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Resources
The Cancer Genome Atlas (TCGA)
The data we used to develop AMARETTO was gathered by The Cancer Genome Atlas (TCGA) a major effort to map the cancer genome of at least 500 patients for more than 20 cancer sites. The TCGA cancer data portal provides access to the multi omics data of all cancer sites.
cBio Portal
The cBio portal provides an interface to multi omics data from The Cancer Genome Atlas (TCGA) and other large scale projects and visualizes multi omics data for your genes of interest. Try it out by investigating one of the driver genes of the ovarian mesenchymal subtype.
Contributors
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