Integration and analysis of genome-scale data from gliomas

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.

Figure 1 |

Techniques for analyzing gene expression data. a | Clustering of gene expression values using a hierarchical clustering algorithm. In hierarchical clustering, distance is first computed for each pair of sample expression values across all genes using a metric such as Euclidean distance. Pairs showing the closest distances are found and these sets of observations are then joined by using a metric of group similarity, such as average or complete linkage. This process is continued until an entire dendogram is formed that describes the relationship and ordering between all samples. b | Differential gene expression is a technique commonly used to determine genes that display statistically significant changes across conditions. A procedure such as the T test is applied to each gene to test for significant changes across conditions. P-values may be converted to q-values using false discovery rate to account for multiple hypothesis testing. c | A gene expression signature can be combined with a classification algorithm, such as linear discriminant analysis or support vector machines. After development on a training set, the model can then be applied to classification of samples in an external test set. This process can be used to assign tumor samples to subtype or generate predictive models of drug response.

Figure 2 |

Copy number alteration detection using SNP chips. a | Tumor samples and reference samples are individually analyzed. Gain or loss of chromosomal regions is computed by comparing signal intensity in the tumor sample with the background signal in the reference and then applying a segmenting and smoothing algorithm. LOH frequently occurs in cancer when an individual carries one functioning copy of a tumor suppressor that is lost through chromosomal deletion. LOH can also be analyzed with SNP chips by detecting SNPs that show a heterozygous to homozygous transition between the reference and tumor sample. b | Classic chromosomal changes seen in glioma, with gain of chromosome 7 containing the EGFR gene and loss of chromosome 10 containing the PTEN gene. c | Chromosomal changes in oligodendrogliomas include 1p and 19q deletion. Abbreviations: LOH, loss of heterozygosity; SNP, single nucleotide polymorphism. Parts a–c were adapted from Kotliarov, Y. et al. BMC Med. Genom. 3, 11 (2010), which is published under an open-access license by Biomed Central.

Figure 3 |

Mapping genomic changes in glioma to known pathways. Mutations and copy number changes from 91 tumors were applied to the known topologies of the RTK–Ras–PI3K, p53, and RB signaling pathways. 88% of patients showed alterations in the RTK–Ras–PI3K pathway, 87% showed alterations in the p53 pathway, and 78% showed alterations in the RB pathway. Overall, 74% of patients showed changes in all three pathways. Red shading indicates activating genetic alterations, with darker colour representing frequently altered genes. Blue colour indicates inactivating alterations, with darker shades representing a higher perentage of alteration. Abbreviations: FOXO, forkhead box protein O; PI3K, phosphoinositide 3-kinase; RB, retinoblastoma; RTK, receptor tyrosine kinase. Permission obtained from Nature Publishing Group © The Cancer Genome Atlas Research Network. Nature 455, 1061–1068 (2008).

Figure 4 |

Mapping genomic changes in glioma to inferred pathways. a | Core module of transcription factors associated with mesenchymal transformation of glioma. b | p53 network in glioma created by combining copy number alteration, mutation, and protein–protein interaction data. c | Network of microRNAs associated with inhibition of the kinase WEE1. Permission for part a obtained from Nature Publishing Group © Carro, M. S. et al. Nature 463, 318–327 (2010). Part b was adapted from Cerami, E. et al. PLoS ONE 5, e8918 (2010) and part c was adapted from Wuchty, S. et al. PLOS ONE 6, e14681 (2010), which are published under an open-access license by Public Library of Science.