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Integration and analysis of genome-scale data from gliomas

Nature Reviews Neurology volume 7, pages 439–450 (2011)Cite this article

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Abstract

Primary brain tumors are a leading cause of cancer-related mortality among young adults and children. The most common primary malignant brain tumor, glioma, carries a median survival of only 14 months. Two major multi-institutional programs, the Glioma Molecular Diagnostic Initiative and The Cancer Genome Atlas, have pursued a comprehensive genomic characterization of a large number of clinical glioma samples using a variety of technologies to measure gene expression, chromosomal copy number alterations, somatic and germline mutations, DNA methylation, microRNA, and proteomic changes. Classification of gliomas on the basis of gene expression has revealed six major subtypes and provided insights into the underlying biology of each subtype. Integration of genome-wide data from different technologies has been used to identify many potential protein targets in this disease, while increasing the reliability and biological interpretability of results. Mapping genomic changes onto both known and inferred cellular networks represents the next level of analysis, and has yielded proteins with key roles in tumorigenesis. Ultimately, the information gained from these approaches will be used to create customized therapeutic regimens for each patient based on the unique genomic signature of the individual tumor. In this Review, we describe efforts to characterize gliomas using genomic data, and consider how insights gained from these analyses promise to increase understanding of the biological underpinnings of the disease and lead the way to new therapeutic strategies.

Key Points

  • Genomic analysis has revealed that gliomas fall into six subtypes, two of which have characteristics of oligodendrogliomas and four of which show poor survival

  • The glioma subtypes show distinct, but overlapping, patterns of mutations, copy number alterations, and gene expression

  • Integration of genomic data, such as gene expression, single nucleotide polymorphism chips, proteomics, microRNA, and epigenomics, increases the reliability and biological interpretability of results

  • Mapping of genomic data onto both known and inferred cellular networks reveals new insights into tumorigenesis

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Figure 1: Techniques for analyzing gene expression data.
Figure 2: Copy number alteration detection using SNP chips.
Figure 3: Mapping genomic changes in glioma to known pathways.
Figure 4: Mapping genomic changes in glioma to inferred pathways.

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  1. The Neuro-Oncology Branch, National Cancer Institute, National Institute of Neurological Disease and Stroke, National Institutes of Health, The Bloch Building, 9030 Old Georgetown Road, Bethesda, 20892, MD, USA

    Gregory Riddick & Howard A. Fine

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Riddick, G., Fine, H. Integration and analysis of genome-scale data from gliomas. Nat Rev Neurol 7, 439–450 (2011). https://doi.org/10.1038/nrneurol.2011.100

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