2-AIN-505, 2-AIN-251: Seminár z bioinformatiky (1) a (3)
Zima 2015
Abstrakt

Fabio Vandin, Eli Upfal, Benjamin J. Raphael. De novo discovery of mutated driver pathways in cancer. Genome research, 22(2):375-375. 2012.

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Abstract:

Next-generation DNA sequencing technologies are enabling genome-wide measurements
of somatic mutations in large numbers of cancer patients. A major challenge in
the interpretation of these data is to distinguish functional \"driver mutations\" 
important for cancer development from random \"passenger mutations.\" A common
approach for identifying driver mutations is to find genes that are mutated at
significant frequency in a large cohort of cancer genomes. This approach is
confounded by the observation that driver mutations target multiple cellular
signaling and regulatory pathways. Thus, each cancer patient may exhibit a
different combination of mutations that are sufficient to perturb these pathways.
This mutational heterogeneity presents a problem for predicting driver mutations 
solely from their frequency of occurrence. We introduce two combinatorial
properties, coverage and exclusivity, that distinguish driver pathways, or groups
of genes containing driver mutations, from groups of genes with passenger
mutations. We derive two algorithms, called Dendrix, to find driver pathways de
novo from somatic mutation data. We apply Dendrix to analyze somatic mutation
data from 623 genes in 188 lung adenocarcinoma patients, 601 genes in 84
glioblastoma patients, and 238 known mutations in 1000 patients with various
cancers. In all data sets, we find groups of genes that are mutated in large
subsets of patients and whose mutations are approximately exclusive. Our Dendrix 
algorithms scale to whole-genome analysis of thousands of patients and thus will 
prove useful for larger data sets to come from The Cancer Genome Atlas (TCGA) and
other large-scale cancer genome sequencing projects.