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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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Overview of RME Module Detection. a) An example of a structural reliability model of progression of a particular tumor type. Cancer progression in this example requires aberrations in each of the three distinct functional modules (three horizontal lines). If mutated genes (crossed out in red) occur in all three modules, the connection between the left and right part of the structural model will be lost, indicating failure (cancer). b) A module may be disrupted by different aberrations in distinct tumor samples. One measure of an RME pattern is coverage, defined as the percentage of samples that contain at least one aberration within the module. Another measure of the pattern is exclusivity, defined as the percentage of covered samples that contain exactly one aberration within the module. An aberration in one of the genes within a specific RME module removes selective pressure of aberrations in other genes within the same module, giving rise to the exclusivity. c) Example network where nodes represent genes and edge thickness represents the level of exclusivity. The search for RME patterns starts by constructing such a graph using the Winnow algorithm. This graph indicates three potential RME modules. The node colors and numbers correspond to those in panel a. d) The significance score for RME patterns is dependent on both exclusivity (y-axis) and coverage (x-axis). Shown is the RME algorithmic compression score, d, for a three-gene RME module across 100 samples with aberrations equally distributed, assuming background frequency of 13.38 aberrant genes per sample (see section 2.3 andAdditional file 1). According to the algorithmic significance test, the significance of an RME pattern is 2-d.
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Figure 1: Overview of RME Module Detection. a) An example of a structural reliability model of progression of a particular tumor type. Cancer progression in this example requires aberrations in each of the three distinct functional modules (three horizontal lines). If mutated genes (crossed out in red) occur in all three modules, the connection between the left and right part of the structural model will be lost, indicating failure (cancer). b) A module may be disrupted by different aberrations in distinct tumor samples. One measure of an RME pattern is coverage, defined as the percentage of samples that contain at least one aberration within the module. Another measure of the pattern is exclusivity, defined as the percentage of covered samples that contain exactly one aberration within the module. An aberration in one of the genes within a specific RME module removes selective pressure of aberrations in other genes within the same module, giving rise to the exclusivity. c) Example network where nodes represent genes and edge thickness represents the level of exclusivity. The search for RME patterns starts by constructing such a graph using the Winnow algorithm. This graph indicates three potential RME modules. The node colors and numbers correspond to those in panel a. d) The significance score for RME patterns is dependent on both exclusivity (y-axis) and coverage (x-axis). Shown is the RME algorithmic compression score, d, for a three-gene RME module across 100 samples with aberrations equally distributed, assuming background frequency of 13.38 aberrant genes per sample (see section 2.3 andAdditional file 1). According to the algorithmic significance test, the significance of an RME pattern is 2-d.

Mentions: To address these issues, we developed a new method for detecting RME patterns, which we formalized by using structural reliability models [12]. Specifically, as illustrated in Figure 1a/b, the RME patterns correspond to modules of the "OR" type in these models, where abrogating the function of one member in each module is sufficient for failure. In our case, "failure" refers to the development of a tumor phenotype. We hypothesized that these RME patterns are sufficiently informative to enable the discovery of cancer-related functional modules without using any prior information. We then tested this hypothesis by designing an algorithm for accurate and computationally efficient detection of these modules. Our tool uses the Winnow algorithm for network construction and establishes significance via the algorithmic significance method, eliminating the need for costly permutation testing. Through simulation experiments, we show that this algorithm scales to very large data sets and evaluate the types of modules that are discoverable using data that will be generated by large tumor characterization projects. We validate our method by applying it to a data set currently available though the TCGA data portal, which consists of mutation and copy number data collected from a cohort of 145 primary GBM tumors. The algorithm identifies known modules from core GBM pathways, extends these modules with new members, and discovers new modules that may inform future studies.


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)

Overview of RME Module Detection. a) An example of a structural reliability model of progression of a particular tumor type. Cancer progression in this example requires aberrations in each of the three distinct functional modules (three horizontal lines). If mutated genes (crossed out in red) occur in all three modules, the connection between the left and right part of the structural model will be lost, indicating failure (cancer). b) A module may be disrupted by different aberrations in distinct tumor samples. One measure of an RME pattern is coverage, defined as the percentage of samples that contain at least one aberration within the module. Another measure of the pattern is exclusivity, defined as the percentage of covered samples that contain exactly one aberration within the module. An aberration in one of the genes within a specific RME module removes selective pressure of aberrations in other genes within the same module, giving rise to the exclusivity. c) Example network where nodes represent genes and edge thickness represents the level of exclusivity. The search for RME patterns starts by constructing such a graph using the Winnow algorithm. This graph indicates three potential RME modules. The node colors and numbers correspond to those in panel a. d) The significance score for RME patterns is dependent on both exclusivity (y-axis) and coverage (x-axis). Shown is the RME algorithmic compression score, d, for a three-gene RME module across 100 samples with aberrations equally distributed, assuming background frequency of 13.38 aberrant genes per sample (see section 2.3 andAdditional file 1). According to the algorithmic significance test, the significance of an RME pattern is 2-d.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 1: Overview of RME Module Detection. a) An example of a structural reliability model of progression of a particular tumor type. Cancer progression in this example requires aberrations in each of the three distinct functional modules (three horizontal lines). If mutated genes (crossed out in red) occur in all three modules, the connection between the left and right part of the structural model will be lost, indicating failure (cancer). b) A module may be disrupted by different aberrations in distinct tumor samples. One measure of an RME pattern is coverage, defined as the percentage of samples that contain at least one aberration within the module. Another measure of the pattern is exclusivity, defined as the percentage of covered samples that contain exactly one aberration within the module. An aberration in one of the genes within a specific RME module removes selective pressure of aberrations in other genes within the same module, giving rise to the exclusivity. c) Example network where nodes represent genes and edge thickness represents the level of exclusivity. The search for RME patterns starts by constructing such a graph using the Winnow algorithm. This graph indicates three potential RME modules. The node colors and numbers correspond to those in panel a. d) The significance score for RME patterns is dependent on both exclusivity (y-axis) and coverage (x-axis). Shown is the RME algorithmic compression score, d, for a three-gene RME module across 100 samples with aberrations equally distributed, assuming background frequency of 13.38 aberrant genes per sample (see section 2.3 andAdditional file 1). According to the algorithmic significance test, the significance of an RME pattern is 2-d.
Mentions: To address these issues, we developed a new method for detecting RME patterns, which we formalized by using structural reliability models [12]. Specifically, as illustrated in Figure 1a/b, the RME patterns correspond to modules of the "OR" type in these models, where abrogating the function of one member in each module is sufficient for failure. In our case, "failure" refers to the development of a tumor phenotype. We hypothesized that these RME patterns are sufficiently informative to enable the discovery of cancer-related functional modules without using any prior information. We then tested this hypothesis by designing an algorithm for accurate and computationally efficient detection of these modules. Our tool uses the Winnow algorithm for network construction and establishes significance via the algorithmic significance method, eliminating the need for costly permutation testing. Through simulation experiments, we show that this algorithm scales to very large data sets and evaluate the types of modules that are discoverable using data that will be generated by large tumor characterization projects. We validate our method by applying it to a data set currently available though the TCGA data portal, which consists of mutation and copy number data collected from a cohort of 145 primary GBM tumors. The algorithm identifies known modules from core GBM pathways, extends these modules with new members, and discovers new modules that may inform future studies.

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