Discovering functional modules by identifying recurrent and mutually exclusive mutational patterns in tumors
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- 作者：Christopher A Miller (1)
Stephen H Settle (2) (3) (6)
Erik P Sulman (3)
Kenneth D Aldape (4)
Aleksandar Milosavljevic (5) - 刊名：BMC Medical Genomics
- 出版年：2011
- 出版时间：December 2011
- 年：2011
- 卷：4
- 期：1
- 全文大小：2267KB
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31. The pre-publication history for this paper can be accessed here:http://www.biomedcentral.com/1755-8794/4/34/prepub - 作者单位：Christopher A Miller (1)
Stephen H Settle (2) (3) (6)
Erik P Sulman (3)
Kenneth D Aldape (4)
Aleksandar Milosavljevic (5)
1. Graduate Program in Structural and Computational Biology and Molecular Biophysics, Baylor College of Medicine, Houston, Texas, USA
2. Department of Molecular and Cellular Biology, Baylor College of Medicine, Houston, Texas, USA
3. Department of Radiation Oncology, the University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
6. Department of Radiation Oncology, the University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
4. Department of Pathology, the University of Texas M. D. Anderson Cancer Center, Houston, Texas, USA
5. Graduate Program in Structural and Computational Biology and Molecular Biophysics; and Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA - ISSN：1755-8794
文摘
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.