Limits...
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.

Show MeSH

Related in: MedlinePlus

Simulation Results. One thousand simulations were run using varied numbers of genes and samples, for 5% and 10% recurrence thresholds. As sample size and the number of genes assayed increase, our algorithm retains the ability to detect RME modules with high sensitivity and precision.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
getmorefigures.php?uid=PMC3102606&req=5

Figure 3: Simulation Results. One thousand simulations were run using varied numbers of genes and samples, for 5% and 10% recurrence thresholds. As sample size and the number of genes assayed increase, our algorithm retains the ability to detect RME modules with high sensitivity and precision.

Mentions: Genes altered in only a few samples did not contain enough information to calculate meaningful exclusivity scores, so we tested two different recurrence thresholds. When considering only genes that are altered in at least 10% of the samples, the algorithm had high sensitivity and precision, with smaller modules being more susceptible to false positives that arise by chance (Figure 3, left column)


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)

Simulation Results. One thousand simulations were run using varied numbers of genes and samples, for 5% and 10% recurrence thresholds. As sample size and the number of genes assayed increase, our algorithm retains the ability to detect RME modules with high sensitivity and precision.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: Simulation Results. One thousand simulations were run using varied numbers of genes and samples, for 5% and 10% recurrence thresholds. As sample size and the number of genes assayed increase, our algorithm retains the ability to detect RME modules with high sensitivity and precision.
Mentions: Genes altered in only a few samples did not contain enough information to calculate meaningful exclusivity scores, so we tested two different recurrence thresholds. When considering only genes that are altered in at least 10% of the samples, the algorithm had high sensitivity and precision, with smaller modules being more susceptible to false positives that arise by chance (Figure 3, left column)

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