Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis

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• 作者：Simon Kebede Merid (3) (4)
  Daria Goranskaya (5) (6)
  Andrey Alexeyenko (7)
  
  3. Master program in Bioinformatics at Department of Biochemistry and Biophysics ; Science for Life Laboratory ; Stockholm University ; Box 1031 ; 171 21 ; Solna ; Sweden
  4. Institute of Environmental Medicine ; Karolinska Institutet ; Box 210 ; SE ; 171 77 ; Stockholm ; Sweden
  5. Department of Biochemistry and Biophysics ; Science for Life Laboratory ; Stockholm University ; Box 1031 ; 171 21 ; Solna ; Sweden
  6. Max Planck Institute for Human Cognitive and Brain Sciences ; Stephanstra脽e 1a ; 04103 ; Leipzig ; Germany
  7. Department of Microbiology ; Tumour and Cell biology ; Bioinformatics Infrastructure for Life Sciences ; Science for Life Laboratory ; Karolinska Institutet ; 17177 ; Stockholm ; Sweden
  
• 关键词：Driver mutations ; Passenger mutations ; Somatic mutations ; Copy number alterations ; Gene networks ; Network analysis ; Cancer ; Glioblastoma ; Ovarian carcinoma ; Brain cell compaction ; Collagen cross ; linking
• 刊名：BMC Bioinformatics
• 出版年：2014
• 出版时间：December 2014
• 年：2014
• 卷：15
• 期：1
• 全文大小：1,660 KB
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• 刊物主题：Bioinformatics; Microarrays; Computational Biology/Bioinformatics; Computer Appl. in Life Sciences; Combinatorial Libraries; Algorithms;
• 出版者：BioMed Central
• ISSN：1471-2105

文摘

Background In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis. Results We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership. Conclusions The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.