Molecular cytogenetics 21
Fig. 2.1 A 1 Mb CGH-array (Sanger Centre) of a retinoblastoma tumor
(Top) Gain of BAC clones mapping to 6p (green). (Bottom) Two regions of gain on 13q (black) and two regions of deletion on 13q (gray).
0 2E+07 4E+07 6E+07 8E+07 1E+08 1E+08 1E+08 2E+08 2E+08
Distance in bp
Distance in bp
above a user-defined threshold. This step facilitates the search for partners and groups in the data that can be used to assign biological meaning to the expression profiles, leading to the production of straightforward lists of increasing or decreasing genes or of more complex associations with the help of sophisticated clustering and visualization programs. Hierarchical clustering is used traditionally in phylogenetic analysis for the classification of organisms into trees; in the microarray context it is applied to genes and samples. Organisms sharing properties tend to be clustered together. The length of a branch containing two organisms can be considered a measure of how different the organisms are. It is possible to classify genes in a similar manner, gathering those whose expression patterns are similar into clusters in the tree. Such mock-phy-logenetic trees are often referred to as 'dendrograms'. Genes can also be grouped on the basis of their expression patterns using k-means clustering. The goal is to produce groups of genes with a high degree of similarity within each group and a low degree of similarity between groups. The self-organizing map (SOM) is a clustering technique similar to k-means clustering, but in addition illustrates the relationship between groups by arranging them in a two-dimensional map. SOMs are useful for visualizing the number of distinct expression patterns in the data. A complex data set can also be reduced to a few specified dimensions by applying multidimensional scaling, so that the relationships between groups can be more effectively visualized.
The first classification of cancer on the basis of gene expression showed that it was possible to distinguish between my-eloid and lymphoid acute leukemias by the use of arrays with approximately 6800 human genes. Since then, the approach has been applied successfully to the classification of hemato-logical malignancies and a large variety of solid tumors. Recently, acute lymphoid leukemias with rearrangements of the MLL gene were shown to have expression patterns that could allow them to be distinguished from ALLs and AMLs without the MLL translocations. Further microarray analysis of AML cases with a favorable outcome—AML M2 with t(8;21), AML M3 or M3v with t(15;17) and AML M4eo with inv(16)—has shown a specific pattern of predictor genes associated with
the three subclasses In a subsequent microarray study, AML leukemia samples were specifically chosen to represent the spectrum of known karyotypes common in AML and included examples with AML-FAB phenotypes from Ml to M5. Hierarchical clustering sorted the profiles into separate groups, each representing one of the major cytogenetic classes in AML (i.e. t(8;21), t(15;17), inv(16), 11q23) and a normal karyotype, as shown in Plate 2.11. Statistical analysis identified genes whose expression was strongly correlated with these chromosomal classes. Importantly in this study, the AMLs with a normal karyotype were characterized by distinctive upregulation of certain members of the class I homeobox A and B gene families, implying a common underlying genetic lesion. These data reveal novel diagnostic and therapeutic targets and demonstrate the potential of microarray-based dissection of AML. The cluster analysis presented here illustrates the potential of expression profiling to distinguish the major subclasses. An important conclusion of expression profiling studies is that the major cytogenetic events in AML have associated expression signatures. This could form the basis of customized DNA arrays designed to classify leukemia.
The authors would like to thank Nigel Carter for his collaboration in the array CGH studies.
FISH on metaphase chromosomes and nuclei
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