Dataset Information

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors.

ABSTRACT:

Background

Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.

Methods

We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME patterns) that arise due to positive selection for key cancer phenotypes. Our algorithm efficiently constructs and searches networks of potential interactions and identifies significant modules (RME modules) by using the algorithmic significance test.

Results

We apply the method to the TCGA collection of 145 glioblastoma samples, resulting in extension of known pathways and discovery of new functional modules. The method predicts a role for EP300 that was previously unknown in glioblastoma. We demonstrate the clinical relevance of these results by validating that expression of EP300 is prognostic, predicting survival independent of age at diagnosis and tumor grade.

Conclusions

We have developed a sensitive, simple, and fast method for automatically detecting functional modules in tumors based solely on patterns of recurrent genomic aberration. Due to its ability to analyze very large amounts of diverse data, we expect it to be increasingly useful when applied to the many tumor panels scheduled to be assayed in the near future.

SUBMITTER: Miller CA

PROVIDER: S-EPMC3102606 | biostudies-literature | 2011 Apr

REPOSITORIES: biostudies-literature

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Publications

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors.

Miller Christopher A CA Settle Stephen H SH Sulman Erik P EP Aldape Kenneth D KD Milosavljevic Aleksandar A

BMC medical genomics 20110414

<h4>Background</h4>Assays of multiple tumor samples frequently reveal recurrent genomic aberrations, including point mutations and copy-number alterations, that affect individual genes. Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.<h4>Methods</h4>We present a method that identifies functional modules without any information other than patterns of recurrent and mutually exclusive aberrations (RME pa ...[more]

PMID: 21489305

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Project description:Coevolutionary networks can encapsulate information about the dynamics of presence and absence of gene families in organisms. Analysis of such networks should reveal fundamental principles underlying the evolution of cellular systems and the functionality of sets of genes. In this study, we describe a new approach for analyzing coevolutionary networks. Our method detects Mutually Exclusive Orthologous Modules (MEOMs). A MEOM is composed of two sets of gene families, each including gene families that tend to appear in the same organisms, such that the two sets tend to mutually exclude each other (if one set appears in a certain organism the second set does not). Thus, a MEOM reflects the evolutionary replacement of one set of genes by another due to reasons such as lineage/environmental specificity, incompatibility, or functional redundancy. We use our method to analyze a coevolutionary network that is based on 383 microorganisms from the three domains of life. As we demonstrate, our method is useful for detecting meaningful evolutionary clades of organisms as well as sets of proteins that interact with each other. Among our results, we report that: 1) MEOMs tend to include gene families whose cellular functions involve transport, energy production, metabolism, and translation, suggesting that changes in the metabolic environments that require adaptation to new sources of energy are central triggers of complex/pathway replacement in evolution. 2) Many MEOMs are related to outer membrane proteins, such proteins are involved in interaction with the environment and could thus be replaced as a result of adaptation. 3) MEOMs tend to separate organisms with large phylogenetic distance but they also separate organisms that live in different ecological niches. 4) Strikingly, although many MEOMs can be identified, there are much fewer cases where the two cliques in the MEOM completely mutually exclude each other, demonstrating the flexibility of protein evolution. 5) CO dehydrogenase and thymidylate synthase and the glycine cleavage genes mutually exclude each other in archaea; this may represent an alternative route for generation of methyl donors for thymidine synthesis.

Dataset Information

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors.

Background

Methods

Results

Conclusions

Publications

Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors.

Similar Datasets

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