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

Miller CA, Settle SH, Sulman EP, Aldape KD, Milosavljevic A - BMC Med Genomics (2011)

Bottom Line: Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate Program in Structural and Computational Biology and Molecular Biophysics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

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.

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Related in: MedlinePlus

Analysis Pipeline. In a preprocessing step, validated SNPs and focal CNAs are combined into a mutation matrix. This matrix is fed into the winnow algorithm, which scores each gene pair by exclusivity, indicated by edge scores in a graph. This graph is then searched for modules up to a specified size and the algorithmic significance is calculated for each potential module. Finally, the most significant modules are reported.
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Figure 2: Analysis Pipeline. In a preprocessing step, validated SNPs and focal CNAs are combined into a mutation matrix. This matrix is fed into the winnow algorithm, which scores each gene pair by exclusivity, indicated by edge scores in a graph. This graph is then searched for modules up to a specified size and the algorithmic significance is calculated for each potential module. Finally, the most significant modules are reported.

Mentions: We designed our algorithm to be capable of utilizing many disparate sources of mutational data, including single-nucleotide polymorphisms, copy-number alterations, and epigenomic modifications. In a pre-processing step, these diverse data types were converted into a single two-dimensional binary "mutation" matrix (Figure 2).


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

Miller CA, Settle SH, Sulman EP, Aldape KD, Milosavljevic A - BMC Med Genomics (2011)

Analysis Pipeline. In a preprocessing step, validated SNPs and focal CNAs are combined into a mutation matrix. This matrix is fed into the winnow algorithm, which scores each gene pair by exclusivity, indicated by edge scores in a graph. This graph is then searched for modules up to a specified size and the algorithmic significance is calculated for each potential module. Finally, the most significant modules are reported.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3102606&req=5

Figure 2: Analysis Pipeline. In a preprocessing step, validated SNPs and focal CNAs are combined into a mutation matrix. This matrix is fed into the winnow algorithm, which scores each gene pair by exclusivity, indicated by edge scores in a graph. This graph is then searched for modules up to a specified size and the algorithmic significance is calculated for each potential module. Finally, the most significant modules are reported.
Mentions: We designed our algorithm to be capable of utilizing many disparate sources of mutational data, including single-nucleotide polymorphisms, copy-number alterations, and epigenomic modifications. In a pre-processing step, these diverse data types were converted into a single two-dimensional binary "mutation" matrix (Figure 2).

Bottom Line: Analyses that extend beyond single genes are often restricted to examining pathways, interactions and functional modules that are already known.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Graduate Program in Structural and Computational Biology and Molecular Biophysics, Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.

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.

Show MeSH
Related in: MedlinePlus