DNA-Microarray Analysis of Brain Cancer: Molecular Classification for Therapy

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DNA-Microarray Analysis of Brain Cancer: Molecular Classification for Therapy
Departments of Pathology, and Laboratory Medicine*,, Neurology, Human, Genetics and the Henry E. Singleton Brain Cancer, Research Program at the, David Geffen School of, Medicine, University of, California Los Angeles,, Los Angeles, California, 90095, USA. Correspondence to P.M. e-mail, pmischel@mednet.ucla.edu, Classification scheme for brain tumours. a, DNA-microarray analysis is most useful when it can be integrated with clinical, imaging and histological data. Substantial, effort is required to develop appropriate databases that contain key clinical information, including patient characteristics, such as age and sex. Brain imaging is routinely undertaken and images are housed in a central database. Histological, photomicrographs document cellular morphology, and clinical data are entered in real time through wireless input, devices to ensure accurate and up-to-date information. Biopsy material is preserved for future analyses, linked to clinical, data and used to extract RNA for large-scale expression analysis using microarrays. DNA microarrays can survey virtually the entire expressed genome. A small amount of high quality RNA from tumour, (or non-tumour) tissue is labelled and hybridized on the surface of a chip, which is composed of spotted cDNA clones or, probes spotted or synthesized on the surface of the chip (oligonucleotide arrays), providing a relatively reproducible and, affordable way to analyse thousands of genes simultaneously (see figure). The availability of high quality, high-density, microarrays, coupled with improved methods for RNA extraction, preservation and labelling, have reduced many of the, inherent technical challenges in microarray studies. The primary hurdles now lie in the interpretation, rather than the, acquisition, of the data. Interpretation of DNA-microarray data is challenging because of its potential for noise and because of the complexity, involved in analysing a data matrix of thousands of elements (typically over 40,000 transcripts in hundreds of tumours). First, the data are normalized so that gene-expression profiles can be compared between samples (individual chips). Then, genes for which expression does not vary meaningfully throughout the experiment, but which can confuse data, interpretation (the `noise\’), are filtered out. To find meaningful patterns, computational methods are used, which might, help to define relevant groups of tumours and/or genes. Hierarchical clustering can be used to identify groups of tumours, or genes with similar global gene-expression profiles. These transcriptionally defined groups can then be probed for, correlations with biological, histological or survival-associated distinctions. This type of analysis, in which groups are, defined entirely on the basis of gene-expression profiles without reference to tumour type or grade, is considered to be, `unsupervised\’. Alternatively, it is possible to identify groups of genes for which expression correlates with a biological,, histological or survival-associated parameter (`supervised analysis\’), Supervised and unsupervised analyses provide, different and often complementary types of information, so most microarray studies use combinations of these, approaches. Unsupervised analysis provides global portraits about the predominant grouping of the data, but data can be, grouped in many ways and the predominant grouping might not be the most biologically relevant structure. Supervised, analysis, by identifying groups of genes that correlate with a relevant parameter, can provide relevant lists of differentially, expressed genes that might highlight important biological differences. A revolution in clinical medicine. Targeting specific pathways. Detecting meaningful genomic signatures. Medulloblastomas. Gliomas. DNA-microarray analyses can identify relevant clinical subsets of gliomas. a, A new concept for biomarkers. Genomic correlates of chemosensitivity. Network analysis. Assembling gene lists into pathways. Current, Future, heterogeneity, stratification of patients, Arrays for everyone, or marker subsets?, Prospects for integrating genomic analysis of brain tumours with clinical-, trial development. a, Towards a personalized medicine. Reproducibility and data sharing. This paper is an excellent review of the concept of, network biology, and describes the quantitative tools, that can be used to analyse networks. This important paper showed that medulloblastomas, can be readily distinguished from other brain tumours,, including morphological mimics, on the basis of gene-, This review provides an overview of the gene-, expression profiling. It also showed that a relatively, expression and signal-transduction alterations in, small number of genes could accurately predict, glioblastoma and suggests potential therapeutic, patient survival and response to therapy. strategies. In a logical continuation of the work described in, reference 59, the authors showed that gene-expression, data can predict the outcome for patients with, medulloblastoma, independent of clinical variables. In this paper, the authors used cDNA-microarray, analysis to identify a potentially crucial and, therapeutically targetable pathway that might, promote metastasis in medulloblastoma. In this paper, the authors compare the global gene-, expression profiles of medulloblastomas that are, derived from a set of genetically defined mouse, crosses, thereby identifying the contribution of a, number of genes, including PTC1, LIG4 and p53, in the, development of medulloblastoma. This paper showed that cDNA-microarray technology, could potentially be used to address diagnostically, confusing gliomas, and that transcriptional, information contains more data about outcome than, does pathological examination. This paper was crucial in defining the role of the, hedgehog pathway in the genesis of, medulloblastoma, and in identifying inhibition of the, This paper highlights the potential of cDNA microarrays, hedgehog pathway as a potential therapy. to detect molecular subsets of morphologically, identical glioblastomas. This outstanding review highlights the genetic, In this paper, the authors demonstrate that gene-, mechanisms that are known to be involved in, expression-based grouping of malignant gliomas is a, medulloblastoma, as well as other paediatric brain, more powerful predictor of survival than pathological, tumours. type, grade or age.

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